Alexander Bucksch

Interests

Research

An increasing human population faces the growing demand for agricultural products and accurate global climate models that account for individual plant morphologies to sustain human life. Both demands are ultimately rooted in an improved understanding of the mechanistic origins of plant development and their resulting phenotypes. Such understanding requires geometric and topological descriptors to characterize plant phenotypes and to link phenotypes to genotypes. However, the current plant phenotyping framework relies on simple length and diameter measurements, which fail to capture the exquisite architecture of plants. My research aims to set new frontiers in combining plant phenotyping with recent results from shape theory at the interface of geometry and topology. The core technical method I use is to expand and apply the mathematical concept of a “shape descriptor” to the plant sciences. Shape descriptors describe the current state and growth of complex structures, including the rich geometric and topological characteristics of plants. More generally, understanding adaptation of plants to their environments is best observed within imaging data capturing the spatial arrangement of plant organs forming the plant phenotype. Spatial arrangements appear in leafs, branches, roots etc. on all biological scales. A full understanding the formation of morphological phenotypes requires analysis of the interplay with the underlying formation processes on cellular and genetic scales. Applying and extending shape theory for plants is the centerpiece of my current work towards unravelling the formation of plant phenotypes. In doing so, I utilize data collected with various imaging instruments from which shapes are extracted to apply shape descriptions.

Courses

Scholarly Contributions

Books

• Bucksch, A. (2018). Optical Approaches to Capture Plant Dynamics in Time, Space, and Across Scales.
• Bucksch, A., & Bucksch, A. K. (2011). Revealing the skeleton from imperfect point clouds.

Chapters

• Thygesen, L. G., Egea, G., & Bucksch, A. (2022).

  Innovative Use of Imaging Techniques within Plant Science

  . In Innovative Use of Imaging Techniques within Plant Science. doi:10.3389/978-2-8325-0952-4
• Razak, K. A., Bucksch, A., Damen, M., Westen, C. v., Straatsma, M., & Jong, S. d. (2013).

  Characterizing Tree Growth Anomaly Induced by Landslides Using LiDAR

  . In Landslide Science and Practice: Volume 1: Landslide Inventory and Susceptibility and Hazard Zoning. doi:10.1007/978-3-642-31325-7_31

Journals/Publications

• Bucksch, A. (2025). Editorial: Innovative use of imaging techniques within plant science. Frontiers in Plant Science.
• Bucksch, A. (2025). Genome-wide association mapping and agronomic impact of cowpea root architecture. Theoretical and Applied Genetics.
• Bucksch, A. (2025). Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling. Frontiers in Plant Science.
• Kawa, D., Thiombiano, B., Shimels, M. Z., Taylor, T., Walmsley, A., Vahldick, H. E., Rybka, D., Leite, M. F., Musa, Z., Bucksch, A., Dini-Andreote, F., Schilder, M., Chen, A. J., Daksa, J., Etalo, D. W., Tessema, T., Kuramae, E. E., Raaijmakers, J. M., Bouwmeester, H., & Brady, S. M. (2024). The soil microbiome modulates the sorghum root metabolome and cellular traits with a concomitant reduction of Striga infection. Cell reports, 113971.
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  Sorghum bicolor is among the most important cereals globally and a staple crop for smallholder farmers in sub-Saharan Africa. Approximately 20% of sorghum yield is lost annually in Africa due to infestation with the root parasitic weed Striga hermonthica. Existing Striga management strategies are not singularly effective and integrated approaches are needed. Here, we demonstrate the functional potential of the soil microbiome to suppress Striga infection in sorghum. We associate this suppression with microbiome-mediated induction of root endodermal suberization and aerenchyma formation and with depletion of haustorium-inducing factors, compounds required for the initial stages of Striga infection. We further identify specific bacterial taxa that trigger the observed Striga-suppressive traits. Collectively, our study describes the importance of the soil microbiome in the early stages of root infection by Striga and pinpoints mechanisms of Striga suppression. These findings open avenues to broaden the effectiveness of integrated Striga management practices.
• Pietrzyk, P., Phan-Udom, N., Chutoe, C., Pingault, L., Roy, A., Libault, M., Saengwilai, P., & Bucksch, A. (2024). DIRT/μ – Automated extraction of root hair traits using combinatorial optimization. Journal of Experimental Botany. doi:10.1101/2024.01.18.576310
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  Abstract Similar to any microscopic appendages, such as cilia or antennae, phenotyping of root hairs has been a challenge due to their complex intersecting arrangements in two-dimensional (2D) images and the technical limitations of automated measurements. Digital Imaging of Root Traits at Microscale (DIRT/μ) addresses this issue by computationally resolving intersections and extracting individual root hairs from 2D microscopy images. This solution enables automatic and precise trait measurements of individual root hairs. DIRT/μ rigorously defines a set of rules to resolve intersecting root hairs and minimizes a newly designed cost function to combinatorically identify each root hair in the microscopy image. As a result, DIRT/μ accurately measures traits such as root hair length (RHL) distribution and root hair density (RHD), which are impractical for manual assessment. We tested DIRT/μ on three datasets to validate its performance and showcase potential applications. By measuring root hair traits in a fraction of the time manual methods require, DIRT/μ eliminates subjective biases from manual measurements. Automating individual root hair extraction accelerates phenotyping and quantifies trait variability within and among plants, creating new possibilities to characterize root hair function and their underlying genetics.
• Swetnam, T. L., Antin, P. B., Bartelme, R., Bucksch, A., Camhy, D., Chism, G., Choi, I., Cooksey, A. M., Cosi, M., Cowen, C., Culshaw-Maurer, M., Davey, R., Davey, S., Devisetty, U., Edgin, T., Edmonds, A., Fedorov, D., Frady, J., Fonner, J., , Gillan, J. K., et al. (2024). CyVerse: Cyberinfrastructure for open science. PLoS computational biology, 20(2), e1011270.
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  CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 124,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in three countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.
• Temme, A. A., Kerr, K. L., Nolting, K. M., Dittmar, E. L., Masalia, R. R., Bucksch, A. K., Burke, J. M., & Donovan, L. A. (2024). The genomic basis of nitrogen utilization efficiency and trait plasticity to improve nutrient stress tolerance in cultivated sunflower. Journal of experimental botany.
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  Maintaining crop productivity is challenging as population growth, climate change, and increasing fertilizer costs necessitate expanding crop production to poorer lands whilst reducing inputs. Enhancing crops' nutrient use efficiency is thus an important goal, but requires a better understanding of related traits and their genetic basis. We investigated variation in low nutrient stress tolerance in a diverse panel of cultivated sunflower genotypes grown under high and low nutrient conditions, assessing relative growth rate (RGR) as performance. We assessed variation in traits related to nitrogen utilization efficiency (NUtE), mass allocation, and leaf elemental content. Across genotypes nutrient limitation generally reduced RGR. Moreover, there was a negative correlation between vigor (RGR in control) and decline in RGR in response to stress. Given this trade-off, we focused on nutrient stress tolerance independent from vigor. This tolerance metric correlated with the change in NUtE, plasticity for a suite of morphological traits, and leaf element content. Genome-wide associations revealed regions associated with variation and plasticity in multiple traits, including two regions with ostensibly additive effects on NUtE change. Our results demonstrate potential avenues for improving sunflower nutrient stress tolerance independent from vigor and highlight specific traits and genomic regions that could play a role in enhancing tolerance.
• Becker, S., Bierman, D., Bucksch, A., Calvert, S., Caprez, A., Chandel, A. K., Craker, B. E., Dorius, S. F., Elsik, C. G., Evans, J., Franz, T. E., Gupta, P., Hoogenboom, G., Jewell, B., Knipe, D., Knipe, R., Kooper, R., Krogmeier, J. V., Lamie, R. D., , Lawrence‐Dill, C. J., et al. (2023). The NAPDC: Stakeholder Input and Strategic Directions. USDA White Paper. doi:10.31219/osf.io/tkg96
• Bucksch, A. (2023). OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis. The Plant Journal.
• Bucksch, A. (2023). Analysis of Branch Morphology, Tree Architecture Among Various Peach Cultivars Utilizing TLS Technology. Hortscience.
• Bucksch, A. (2023). Comparison of open‐source three‐dimensional reconstruction pipelines for maize‐root phenotyping. The Plant Phenome Journal.
• Bucksch, A. (2023). Increasing racial diversity in the North American Plant Phenotyping Network through conference participation support. The Plant Phenome Journal.
• Bucksch, A. (2023). Three‐dimensional phenotyping of peach tree‐crown architecture utilizing terrestrial laser scanning. The Plant Phenome Journal.
• Consortium, I. (2023). Inclusive collaboration across plant physiology and genomics: Now is the time!. Plant Direct, 7(5), e493.
• Kawa, D., Thiombiano, B., Shimels, M., Taylor, T., Walmsley, A., Vahldick, H., Leite, M. F., Musa, Z., Bucksch, A., Dini‐Andreote, F., Chen, A. J., Daksa, J., Etalo, D. W., Tessema, T., Kuramae, E., Raaijmakers, J., Bouwmeester, H. J., & Brady, S. M. (2023).

  The Soil Microbiome Reduces Striga Infection of Sorghum by Modulation of Host-Derived Signaling Molecules and Root Development

  . Cell Reports. doi:10.2139/ssrn.4350137
• Knapp-Wilson, J., Reckziegel, R. B., Magar, S. T., Bucksch, A., & Chavez, D. (2023). Three-dimensional phenotyping of peach tree crown architecture utilizing terrestrial laser scanning. The Plant Phenome Journal.
• LeBauer, D., Bucksch, A., Clarke, J., Potts, J., & Roy, S. (2023). Increasing racial diversity in the North American Plant Phenotyping Network through conference participation support. The Plant Phenome Journal, 6(1), e20075.
• LeBauer, D., Bucksch, A., Clarke, J., Potts, J., & Roy, S. (2023). Increasing racial diversity in the North American Plant Phenotyping Network through conference participation support. The Plant Phenome Journal, 6(1). doi:10.1002/ppj2.20075
• Liu, S., Bonelli, W. P., Pietrzyk, P., & Bucksch, A. (2023). Comparison of open-source three-dimensional reconstruction pipelines for maize-root phenotyping. The Plant Phenome Journal, 6(1), e20068.
• Swartz, L. G., Liu, S., Dahlquist, D., Kramer, S. T., Walter, E. S., McInturf, S. A., Bucksch, A., & Mendoza-Cózatl, D. G. (2023). OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis. The Plant journal.
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  The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.
• Swetnam, T. L., Antin, P. B., Bartelme, R. P., Bucksch, A., Camhy, D., Chism, G., Choi, I., Cooksey, A. M., Cosi, M., Cowen, C., Culshaw-Maurer, M., Davey, R. P., Davey, S., Devisetty, U. K., Edgin, T., Edmonds, A., Fedorov, D. V., Frady, J., Fonner, J. M., , Gillan, J. K., et al. (2023).

  CyVerse: Cyberinfrastructure for Open Science

  . PLoS Computational Biology. doi:10.1101/2023.06.16.545223
  More info

  Abstract CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 110,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in two countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.
• Swetnam, T. L., Antin, P. B., Bartelme, R., Bucksch, A., Camhy, D., Chism, G., Choi, I., Cooksey, A. M., Cosi, M., & Cowen, C. (2024). CyVerse: Cyberinfrastructure for Open Science. PloS Computational Biology. doi:10.1371/journal.pcbi.1011270
• Bonelli, W., Liu, S., Cotter, C., Flory, M., Luck, M., & Bucksch, A. (2022). PlantIT: Containerized phenotyping in the cloud. Authorea Preprints.
• Bucksch, A. (2022). 3D imaging and quantitative analysis of peach tree architecture via TreeQSM. Acta Horticulturae.
• Bucksch, A. (2022). Root angle in maize influences nitrogen capture and is regulated by calcineurin B-like protein (CBL)-interacting serine/threonine-protein kinase 15 (ZmCIPK15).. Plant, Cell and Environment.
• Egea, G., Bucksch, A., & Thygesen, L. G. (2022). Editorial: Innovative use of imaging techniques within plant science. Frontiers in plant science, 13, 1079022.
• Egea, G., Bucksch, A., & Thygesen, L. G. (2022). Innovative use of imaging techniques within plant science. Innovative Use of Imaging Techniques within Plant Science, 16648714, 4.
• Kawa, D., Thiombiano, B., Shimels, M., Taylor, T., Walmsley, A., Vahldick, H. E., Leite, M. F., Musa, Z., Bucksch, A., Dini-Andreote, F., & others, . (2022). The soil microbiome reduces Striga infection of sorghum by modulation of host-derived signaling molecules and root development. bioRxiv, 2022--11.
• Kengkanna, J., Bucksch, A., & Saengwilai, P. J. (2022). Cassava root phenotyping for arsenic phytoremediation. Authorea Preprints.
• Knapp-Wilson, J., Reckziegel, R. B., Bucksch, A., & Chavez, D. J. (2022). 3D phenotyping of peach tree canopy architecture using terrestrial laser scanning. Authorea Preprints.
• Knapp‐Wilson, J., Reckziegel, R. B., Bucksch, A., & Chavez, D. J. (2022). 3D imaging and quantitative analysis of peach tree architecture via TreeQSM. Acta Horticulturae. doi:10.17660/actahortic.2022.1352.41
• LaVoy, W., Xie, L., Liu, S., & Bucksch, A. (2022). A Platform to Quantify Phenotypic Responses to Root-Root Interactions Among Common Beans.
• Liu, S., Bonelli, W. P., Pietrzyk, P., & Bucksch, A. (2022).

  Comparison of Open-Source Three-Dimensional Reconstruction Pipelines for Maize-Root Phenotyping

  . The Plant Phenome Journal. doi:10.1002/essoar.10512880.1
• Pietrzyk, P., Phan-Udom, N., Chutoe, C., Saengwilai, P., & Bucksch, A. (2022). DIRT/mu: Automatic root hair measurement in maize (Zea mays ssp.) from microscopy images. Authorea Preprints.
• Roy, A., Bralick, A., Pietrzyk, P., & Bucksch, A. (2022). The morphological phenotyping of hooked hairs in Phaseolus Vulgaris. Authorea Preprints.
• Schneider, H. M., Lor, V. S., Hanlon, M. T., Perkins, A., Kaeppler, S. M., Borkar, A. N., Bhosale, R., Zhang, X., Rodriguez, J., Bucksch, A., Bennett, M. J., Brown, K. M., & Lynch, J. P. (2022). Root angle in maize influences nitrogen capture and is regulated by calcineurin B-like protein (CBL)-interacting serine/threonine-protein kinase 15 (ZmCIPK15). Plant, cell & environment, 45(3), 837-853.
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  Crops with reduced nutrient and water requirements are urgently needed in global agriculture. Root growth angle plays an important role in nutrient and water acquisition. A maize diversity panel of 481 genotypes was screened for variation in root angle employing a high-throughput field phenotyping platform. Genome-wide association mapping identified several single nucleotide polymorphisms (SNPs) associated with root angle, including one located in the root expressed CBL-interacting serine/threonine-protein kinase 15 (ZmCIPK15) gene (LOC100285495). Reverse genetic studies validated the functional importance of ZmCIPK15, causing a approximately 10° change in root angle in specific nodal positions. A steeper root growth angle improved nitrogen capture in silico and in the field. OpenSimRoot simulations predicted at 40 days of growth that this change in angle would improve nitrogen uptake by 11% and plant biomass by 4% in low nitrogen conditions. In field studies under suboptimal N availability, the cipk15 mutant with steeper growth angles had 18% greater shoot biomass and 29% greater shoot nitrogen accumulation compared to the wild type after 70 days of growth. We propose that a steeper root growth angle modulated by ZmCIPK15 will facilitate efforts to develop new crop varieties with optimal root architecture for improved performance under edaphic stress.
• Swartz, L. G., Liu, S., Dahlquist, D., Walter, E. S., Mcinturf, S., Bucksch, A., & Mendoza-Cozatl, D. G. (2022). Tracking dynamic changes of leaves in response to nutrient availability using an open-source cloud-based phenotyping system (OPEN Leaf). Authorea Preprints.
• Temme, A. A., Kerr, K. L., Nolting, K. M., Dittmar, E. L., Masalia, R. R., Bucksch, A. K., Burke, J. M., & Donovan, L. A. (2022). Exceeding expectations: the genomic basis of nitrogen utilization efficiency and integrated trait plasticity as avenues to improve nutrient stress tolerance in cultivated sunflower (Helianthus annuus L.). bioRxiv, 2022--08.
• Bucksch, A. (2021). Characterization of growth and development of sorghum genotypes with differential susceptibility to Striga hermonthica. Journal of Experimental Botany.
• Bucksch, A. (2021). DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays). Plant Physiology.
• Bucksch, A. (2021). Root hairs vs. trichomes: Not everyone is straight!. Current Opinion in Plant Biology.
• Dale, R., Oswald, S., Jalihal, A., LaPorte, M. F., Fletcher, D. M., Hubbard, A., Shiu, S. H., Nelson, A. D., & Bucksch, A. (2021). Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling. Frontiers in plant science, 12, 687652.
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  The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.
• Dale, R., Oswald, S., Jalihal, A., Laporte, M., Fletcher, D. M., Hubbard, A., Shiu, S., Nelson, A. D., & Bucksch, A. (2021). Overcoming the challenges to enhancing experimental plant biology with computational modeling. Frontiers in Plant Science, 12, 1266.
• Kawa, D., Taylor, T., Thiombiano, B., Musa, Z., Vahldick, H. E., Walmsley, A., Bucksch, A., Bouwmeester, H., & Brady, S. M. (2021). Characterization of growth and development of sorghum genotypes with differential susceptibility to Striga hermonthica. Journal of Experimental Botany, 72(22), 7970--7983.
• Kawa, D., Taylor, T., Thiombiano, B., Musa, Z., Vahldick, H. E., Walmsley, A., Bucksch, A., Bouwmeester, H., & Brady, S. M. (2021). Characterization of growth and development of sorghum genotypes with differential susceptibility to Striga hermonthica. Journal of experimental botany, 72(22), 7970-7983.
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  Two sorghum varieties, Shanqui Red (SQR) and SRN39, have distinct levels of susceptibility to the parasitic weed Striga hermonthica, which have been attributed to different strigolactone composition within their root exudates. Root exudates of the Striga-susceptible variety Shanqui Red (SQR) contain primarily 5-deoxystrigol, which has a high efficiency for inducing Striga germination. SRN39 roots primarily exude orobanchol, leading to reduced Striga germination and making this variety resistant to Striga. The structural diversity in exuded strigolactones is determined by a polymorphism in the LOW GERMINATION STIMULANT 1 (LGS1) locus. Yet, the genetic diversity between SQR and SRN39 is broad and has not been addressed in terms of growth and development. Here, we demonstrate additional differences between SQR and SRN39 by phenotypic and molecular characterization. A suite of genes related to metabolism was differentially expressed between SQR and SRN39. Increased levels of gibberellin precursors in SRN39 were accompanied by slower growth rate and developmental delay and we observed an overall increased SRN39 biomass. The slow-down in growth and differences in transcriptome profiles of SRN39 were strongly associated with plant age. Additionally, enhanced lateral root growth was observed in SRN39 and three additional genotypes exuding primarily orobanchol. In summary, we demonstrate that the differences between SQR and SRN39 reach further than the changes in strigolactone profile in the root exudate and translate into alterations in growth and development.
• Liu, S., Barrow, C. S., Hanlon, M. T., Lynch, J. P., & Bucksch, A. (2021).

  DIRT/3D: 3D root phenotyping for field grown maize (Zea mays)

  . Plant Physiology. doi:10.1101/2020.06.30.180059
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  Abstract The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs and, to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with DIRT/3D, a newly developed image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize root crowns excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r 2 >0.84 and a high broad-sense heritability of for all but one trait. The average values of the 18 traits and a newly developed descriptor to characterize a complete root architecture distinguished all genotypes. DIRT/3D is a step towards automated quantification of highly occluded maize root crowns. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.
• Liu, S., Barrow, C. S., Hanlon, M., Lynch, J. P., & Bucksch, A. (2021). DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays). Plant physiology, 187(2), 739-757.
  More info

  The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.
• Roy, A., & Bucksch, A. (2021). Root hairs vs. trichomes: Not everyone is straight!. Current opinion in plant biology, 64, 102151.
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  Trichomes show 47 morphological phenotypes, while literature reports only two root hair phenotypes in all plants. However, could hair-like structures exist below-ground in a similar wide range of morphologies like trichomes? Genetic mutants and root hair stress phenotypes point to the possibility of uncharacterized morphological variation existing belowground. For example, such root hairs in Arabidopsis (Arabidopsis thaliana) can be wavy, curled, or branched. We found hints in the literature about hair-like structures that emerge before root hairs belowground. As such, these early emerging hair structures can be potential exceptions to the contrasting morphological variation between trichomes and root hairs. Here, we show a previously unreported 'hooked' hair structure growing below-ground in common bean. The unique 'hooking' shape distinguishes the 'hooked hair' morphologically from root hairs. Currently, we cannot fully characterize the phenotype of our observation due to the lack of automated methods for phenotyping root hairs. This phenotyping bottleneck also handicaps the discovery of more morphology types that might exist below-ground as manual screening across species is slower than computer-assisted high-throughput screening.
• Schneider, H., Lor, V. S., Hanlon, M. T., Perkins, A., Kaeppler, S. M., Borkar, A. N., Bhosale, R., Zhang, X., Rodriguez, J., Bucksch, A., Bennett, M., Brown, K. M., & Lynch, J. P. (2021).

  Root angle in maize influences nitrogen capture and is regulated by calcineurin B‐like protein (CBL)‐interacting serine/threonine‐protein kinase 15 (ZmCIPK15)

  . Plant Cell & Enviroment. doi:10.1111/pce.14135
  More info

  Crops with reduced nutrient and water requirements are urgently needed in global agriculture. Root growth angle plays an important role in nutrient and water acquisition. A maize diversity panel of 481 genotypes was screened for variation in root angle employing a high-throughput field phenotyping platform. Genome-wide association mapping identified several single nucleotide polymorphisms (SNPs) associated with root angle, including one located in the root expressed CBL-interacting serine/threonine-protein kinase 15 (ZmCIPK15) gene (LOC100285495). Reverse genetic studies validated the functional importance of ZmCIPK15, causing a approximately 10° change in root angle in specific nodal positions. A steeper root growth angle improved nitrogen capture in silico and in the field. OpenSimRoot simulations predicted at 40 days of growth that this change in angle would improve nitrogen uptake by 11% and plant biomass by 4% in low nitrogen conditions. In field studies under suboptimal N availability, the cipk15 mutant with steeper growth angles had 18% greater shoot biomass and 29% greater shoot nitrogen accumulation compared to the wild type after 70 days of growth. We propose that a steeper root growth angle modulated by ZmCIPK15 will facilitate efforts to develop new crop varieties with optimal root architecture for improved performance under edaphic stress.
• Bucksch, A. (2020). Canopy Roughness: A New Phenotypic Trait to Estimate Aboveground Biomass from Unmanned Aerial System. Plant Phenomics.
• Bucksch, A. (2020). From lab to field: Open tools facilitating the translation of maize root traits. Field Crops Research.
• Bucksch, A. (2020). Image‐based root phenotyping links root architecture to micronutrient concentration in cassava. PLANTS, PEOPLE, PLANET.
• Busener, N., Kengkanna, J., Saengwilai, P. J., & Bucksch, A. (2020). Image-based root phenotyping links root architecture to micronutrient concentration in cassava. Plants, People, Planet, 2(6), 678--687.
• Busener, N., Kengkanna, J., Saengwilai, P., & Bucksch, A. (2020). Image‐based root phenotyping links root architecture to micronutrient concentration in cassava. Plants, People, Planet. doi:10.1002/ppp3.10130
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  Societal Impact Statement Micronutrient deficiency or “hidden hunger” is estimated to affect two billion people worldwide and increasing the micronutrient concentration of food could play an important role in tackling this global challenge. Using a combination of imaging techniques and atomic absorption spectroscopy, we describe a link between root phenotype and micronutrient concentration in cassava, which could enable new phenotypic selection strategies for breeding. This approach could be used with existing breeding infrastructure to enhance the micronutrient concentration of cassava and hence, benefit the health of people, particularly in low‐income countries where cassava is consumed as a staple crop. Summary Cassava storage roots are a staple food in low‐income countries of South‐East Asia and sub‐Saharan Africa, where growth stunting is prevalent as a consequence of micronutrient deficiencies. We aim to link phenotypes of field‐grown cassava roots to micronutrient concentration in the edible storage roots as a simple way to improve phenotypic selection for nutritional value in cassava. We used existing and newly developed imaging techniques to quantify root phenotypes of the cassava root architecture over time and used flame atomic absorption spectroscopy to measure micronutrient concentration in storage roots. Both together allow the association of root phenotypes with micronutrient concentration in mature cassava roots. We show that early and late bulking genotypes in cassava exhibit distinct foraging behaviors that are associated with micronutrient concentration in the edible storage root. Our observations suggest that late bulking cassava is a key to provide sufficient micronutrients in the edible storage root. The association between root phenotype and micronutrient concentration with imaging techniques allows phenotypic selection for enhanced micronutrient concentration. Therefore, implementing image‐based phenotyping into cassava breeding programs in sub‐Saharan Africa and South‐East Asia could be an essential element to resolve micronutrient deficiencies that puts individuals at a higher risk of growth stunting.
• Herrero-Huerta, M., Bucksch, A., Puttonen, E., & Rainey, K. M. (2020). Canopy Roughness: A New Phenotypic Trait to Estimate Aboveground Biomass from Unmanned Aerial System. Plant phenomics (Washington, D.C.), 2020, 6735967.
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  Cost-effective phenotyping methods are urgently needed to advance crop genetics in order to meet the food, fuel, and fiber demands of the coming decades. Concretely, characterizing plot level traits in fields is of particular interest. Recent developments in high-resolution imaging sensors for UAS (unmanned aerial systems) focused on collecting detailed phenotypic measurements are a potential solution. We introduce canopy roughness as a new plant plot-level trait. We tested its usability with soybean by optical data collected from UAS to estimate biomass. We validate canopy roughness on a panel of 108 soybean [Glycine max (L.) Merr.] recombinant inbred lines in a multienvironment trial during the R2 growth stage. A senseFly eBee UAS platform obtained aerial images with a senseFly S.O.D.A. compact digital camera. Using a structure from motion (SfM) technique, we reconstructed 3D point clouds of the soybean experiment. A novel pipeline for feature extraction was developed to compute canopy roughness from point clouds. We used regression analysis to correlate canopy roughness with field-measured aboveground biomass (AGB) with a leave-one-out cross-validation. Overall, our models achieved a coefficient of determination ( ) greater than 0.5 in all trials. Moreover, we found that canopy roughness has the ability to discern AGB variations among different genotypes. Our test trials demonstrate the potential of canopy roughness as a reliable trait for high-throughput phenotyping to estimate AGB. As such, canopy roughness provides practical information to breeders in order to select phenotypes on the basis of UAS data.
• Salungyu, J., Thaitad, S., Bucksch, A., Kengkanna, J., & Saengwilai, P. J. (2020). From lab to field: Open tools facilitating the translation of maize root traits. Field Crops Research, 255, 107872.
• Bucksch, A. (2019). Architectural and anatomical responses of maize roots to agronomic practices in a semi‐arid environment. Journal of Plant Nutrition and Soil Science.
• Bucksch, A. (2019). Phenotypic variation of cassava root traits and their responses to drought. Applications in Plant Sciences.
• Bucksch, A. (2019). Phenotyping physiological and morphological traits of the Arabidopsis rosetta with a fully parameter-free algorithm.
• Kengkanna, J., Jakaew, P., Amawan, S., Busener, N., Bucksch, A., & Saengwilai, P. (2019). Phenotypic variation of cassava root traits and their responses to drought. Applications in plant sciences, 7(4), e01238.
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  The key to increased cassava production is balancing the trade-off between marketable roots and traits that drive nutrient and water uptake. However, only a small number of protocols have been developed for cassava roots. Here, we introduce a set of new variables and methods to phenotype cassava roots and enhance breeding pipelines.
• Zhan, A. i., Liu, J., Yue, S., Chen, X., Li, S., & Bucksch, A. (2019). Architectural and anatomical responses of maize roots to agronomic practices in a semi-arid environment. Journal of Plant Nutrition and Soil Science.
• Bucksch, A. (2018). Comparing phenotypic variation of root traits in Thai rice (Oryza sativa L.) across growing systems. Applied Ecology and Environmental Research.
• Bucksch, A. (2018). Editorial: Optical Approaches to Capture Plant Dynamics in Time, Space, and Across Scales. Frontiers in Plant Science.
• Puttonen, E., Bucksch, A., Zlinszky, A., & Pfeifer, N. (2018). Editorial: Optical Approaches to Capture Plant Dynamics in Time, Space, and Across Scales. Frontiers in plant science, 9, 791.
• Saengwilai, P., Klinsawang, S., Sangachart, M., Bucksch, A., & others, . (2018). Comparing phenotypic variation of root traits in Thai rice (Oryza sativa L.) across growing systems. Appl. Ecol. Environ. Res, 16, 1069--1083.
• Sangachart, M., Wannaro, A., Satarn, S., Jaruwatee, S., Bucksch, A., & Saengwilai, P. (2018).

  COMPARING PHENOTYPIC VARIATION OF ROOT TRAITS IN THAI RICE (ORYZA SATIVA L.) ACROSS GROWING SYSTEMS

  . APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, 16(2), 1069-1083.
• Balduzzi, M., Binder, B. M., Bucksch, A., Chang, C., Hong, L., Iyer-Pascuzzi, A. S., Pradal, C., & Sparks, E. E. (2017). Reshaping Plant Biology: Qualitative and Quantitative Descriptors for Plant Morphology. Frontiers in plant science, 8, 117.
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  An emerging challenge in plant biology is to develop qualitative and quantitative measures to describe the appearance of plants through the integration of mathematics and biology. A major hurdle in developing these metrics is finding common terminology across fields. In this review, we define approaches for analyzing plant geometry, topology, and shape, and provide examples for how these terms have been and can be applied to plants. In leaf morphological quantifications both geometry and shape have been used to gain insight into leaf function and evolution. For the analysis of cell growth and expansion, we highlight the utility of geometric descriptors for understanding sepal and hypocotyl development. For branched structures, we describe how topology has been applied to quantify root system architecture to lend insight into root function. Lastly, we discuss the importance of using morphological descriptors in ecology to assess how communities interact, function, and respond within different environments. This review aims to provide a basic description of the mathematical principles underlying morphological quantifications.
• Bucksch, A. (2017). Morphological plant modeling: Unleashing geometric and topological potential within the plant sciences. Frontiers in Plant Science.
• Bucksch, A. (2017). Overcoming the Law of the Hidden in Cyberinfrastructures. Trends in Plant Science.
• Bucksch, A. (2017). Reshaping Plant Biology: Qualitative and Quantitative Descriptors for Plant Morphology. Frontiers in Plant Science.
• Bucksch, A. (2017). The Next Generation of Training for Arabidopsis Researchers: Bioinformatics and Quantitative Biology. Plant Physiology.
• Bucksch, A., Atta-Boateng, A., Azihou, A. F., Battogtokh, D., Baumgartner, A., Binder, B. M., Braybrook, S. A., Chang, C., Coneva, V., DeWitt, T. J., Fletcher, A. G., Gehan, M. A., Diaz-Martinez, D. H., Hong, L., Iyer-Pascuzzi, A. S., Klein, L. L., Leiboff, S., Li, M., Lynch, J. P., , Maizel, A., et al. (2017). Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences. Frontiers in plant science, 8, 900.
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  The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics.
• Bucksch, A., Das, A., Schneider, H., Weitz, J. S., & Merchant, N. C. (2017). Overcoming the Law of the Hidden in Cyberinfrastructures. Trends in plant science, 22(2), 117-123.
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  Cyberinfrastructure projects (CIPs) are complex, integrated systems that require interaction and organization amongst user, developer, hardware, technical infrastructure, and funding resources. Nevertheless, CIP usability, functionality, and growth do not scale with the sum of these resources. Instead, growth and efficient usage of CIPs require access to 'hidden' resources. These include technical resources within CIPs as well as social and functional interactions among stakeholders. We identify approaches to overcome resource limitations following the conceptual basis of Liebig's Law of the Minimum. In so doing, we recommend practical steps towards efficient and scaleable resource use, taking the iPlant/CyVerse CIP as an example.
• Burridge, J. D., Schneider, H. M., Huynh, B. L., Roberts, P. A., Bucksch, A., & Lynch, J. P. (2017). Genome-wide association mapping and agronomic impact of cowpea root architecture. TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 130(2), 419-431.
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  Genetic analysis of data produced by novel root phenotyping tools was used to establish relationships between cowpea root traits and performance indicators as well between root traits and Striga tolerance. Selection and breeding for better root phenotypes can improve acquisition of soil resources and hence crop production in marginal environments. We hypothesized that biologically relevant variation is measurable in cowpea root architecture. This study implemented manual phenotyping (shovelomics) and automated image phenotyping (DIRT) on a 189-entry diversity panel of cowpea to reveal biologically important variation and genome regions affecting root architecture phenes. Significant variation in root phenes was found and relatively high heritabilities were detected for root traits assessed manually (0.4 for nodulation and 0.8 for number of larger laterals) as well as repeatability traits phenotyped via DIRT (0.5 for a measure of root width and 0.3 for a measure of root tips). Genome-wide association study identified 11 significant quantitative trait loci (QTL) from manually scored root architecture traits and 21 QTL from root architecture traits phenotyped by DIRT image analysis. Subsequent comparisons of results from this root study with other field studies revealed QTL co-localizations between root traits and performance indicators including seed weight per plant, pod number, and Striga (Striga gesnerioides) tolerance. The data suggest selection for root phenotypes could be employed by breeding programs to improve production in multiple constraint environments.
• Friesner, J., Friesner, J., Assmann, S. M., Assmann, S. M., Bastow, R., Bastow, R., Bailey-Serres, J., Bailey-Serres, J., Beynon, J., Beynon, J., Brendel, V., Brendel, V., Buell, C. R., Buell, C. R., Bucksch, A., Bucksch, A., Busch, W., Busch, W., Demura, T., , Demura, T., et al. (2017). The Next Generation of Training for Arabidopsis Researchers: Bioinformatics and Quantitative Biology. Plant physiology, 175(4), 1499-1509.
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  Training for experimental plant biologists needs to combine bioinformatics, quantitative approaches, computational biology, and training in the art of collaboration, best achieved through fully integrated curriculum development.
• Bucksch, A. (2016). Morphological plant modeling: Unleashing geometric and topologic potential within the plant sciences.
• Bucksch, A., Atta-Boateng, A., Azihou, A. F., Balduzzi, M., Battogtokh, D., Baumgartner, A., Binder, B. M., Braybrook, S. A., Chang, C., Coneva, V., Azhari, A., Fletcher, A. G., Gehan, M., Martínez, D. H., Hong, L., Iyer‐Pascuzzi, A. S., Klein, L. L., Leiboff, S., Mao, L., , Lynch, J. P., et al. (2016).

  Morphological plant modeling: Unleashing geometric and topological potential within the plant sciences

  . Frontiers in Plant Science. doi:10.1101/078832
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  Abstract Plant morphology is inherently mathematical in that morphology describes plant form and architecture with geometrical and topological descriptors. The geometries and topologies of leaves, flowers, roots, shoots and their spatial arrangements have fascinated plant biologists and mathematicians alike. Beyond providing aesthetic inspiration, quantifying plant morphology has become pressing in an era of climate change and a growing human population. Modifying plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems with fewer natural resources. In this white paper, we begin with an overview of the mathematical models applied to quantify patterning in plants. We then explore fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leafs in air streams. We end with a discussion concerning the incorporation of plant morphology into educational programs. This strategy focuses on synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. This white paper arose from bringing mathematicians and biologists together at the National Institute for Mathematical and Biological Synthesis (NIMBioS) workshop titled “Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences” held at the University of Tennessee, Knoxville in September, 2015. Never has the need to quantify plant morphology been more imperative. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics.
• Burridge, J., Jochua, C. N., Bucksch, A., & Lynch, J. P. (2016). Legume shovelomics: high???throughput phenotyping of common bean (Phaseolus vulgaris L.) and cowpea (Vigna unguiculata subsp, unguiculata) root architecture in the field. Field Crops Research, 192, 21--32.
• Burridge, J., Schneider, H., Huynh, B. L., Roberts, P., Bucksch, A., & Lynch, J. (2016). Cowpea Root Architecture Has Agronomic Impact.
• Bucksch, A. (2015). Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics. Plant Methods.
• Das, A., Schneider, H., Burridge, J., Ascanio, A. K., Wojciechowski, T., Topp, C. N., Lynch, J. P., Weitz, J. S., & Bucksch, A. (2015). Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics. Plant Methods. doi:10.1186/s13007-015-0093-3
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  Plant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput. Here, we present an open-source phenomics platform "DIRT", as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute "commons" enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size. DIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://dirt.iplantcollaborative.org/ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science.
• Das, A., Schneider, H., Burridge, J., Ascanio, A. K., Wojciechowski, T., Topp, C. N., Lynch, J. P., Weitz, J. S., & Bucksch, A. (2015). Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics. Plant methods, 11, 51.
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  Plant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput.
• Das, A., Schneider, H., Burridge, J., Ascanio, A., Wojciechowski, T., Topp, C. N., Lynch, J. P., Weitz, J. S., & Bucksch, A. (2015). DIRT: a high-throughput computing and collaboration platform for field-based root phenomics. Plant Methods, 11(51).
• Bucksch, A. (2014).

  A Practical Introduction to Skeletons for the Plant Sciences

  . Applications in Plant Science. doi:10.3732/apps.1400005
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  Before the availability of digital photography resulting from the invention of charged couple devices in 1969, the measurement of plant architecture was a manual process either on the plant itself or on traditional photographs. The introduction of cheap digital imaging devices for the consumer market enabled the wide use of digital images to capture the shape of plant networks such as roots, tree crowns, or leaf venation. Plant networks contain geometric traits that can establish links to genetic or physiological characteristics, support plant breeding efforts, drive evolutionary studies, or serve as input to plant growth simulations. Typically, traits are encoded in shape descriptors that are computed from imaging data. Skeletons are one class of shape descriptors that are used to describe the hierarchies and extent of branching and looping plant networks. While the mathematical understanding of skeletons is well developed, their application within the plant sciences remains challenging because the quality of the measurement depends partly on the interpretation of the skeleton. This article is meant to bridge the skeletonization literature in the plant sciences and related technical fields by discussing best practices for deriving diameters and approximating branching hierarchies in a plant network.
• Bucksch, A. (2014). A practical introduction to skeletons for the plant sciences. Applications in plant sciences, 2(8).
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  Before the availability of digital photography resulting from the invention of charged couple devices in 1969, the measurement of plant architecture was a manual process either on the plant itself or on traditional photographs. The introduction of cheap digital imaging devices for the consumer market enabled the wide use of digital images to capture the shape of plant networks such as roots, tree crowns, or leaf venation. Plant networks contain geometric traits that can establish links to genetic or physiological characteristics, support plant breeding efforts, drive evolutionary studies, or serve as input to plant growth simulations. Typically, traits are encoded in shape descriptors that are computed from imaging data. Skeletons are one class of shape descriptors that are used to describe the hierarchies and extent of branching and looping plant networks. While the mathematical understanding of skeletons is well developed, their application within the plant sciences remains challenging because the quality of the measurement depends partly on the interpretation of the skeleton. This article is meant to bridge the skeletonization literature in the plant sciences and related technical fields by discussing best practices for deriving diameters and approximating branching hierarchies in a plant network.
• Bucksch, A. (2014). Breast height diameter estimation from high-density airborne LiDAR data. IEEE Geoscience and Remote Sensing Letters.
• Bucksch, A. (2014). Image-based high-throughput field phenotyping of crop roots. Plant Physiol.
• Bucksch, A. (2014). The Fiber Walk: A Model of Tip-Driven Growth with Lateral Expansion. PLoS ONE.
• Bucksch, A., Burridge, J., York, L. M., Das, A., Nord, E. A., Weitz, J. S., & Lynch, J. P. (2014). Image-Based High-Throughput Field Phenotyping of Crop Roots. Plant Physiology. doi:10.1104/pp.114.243519
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  Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.
• Bucksch, A., Burridge, J., York, L. M., Das, A., Nord, E., Weitz, J. S., & Lynch, J. P. (2014). Image-based high-throughput field phenotyping of crop roots. Plant physiology, 166(2), 470-86.
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  Current plant phenotyping technologies to characterize agriculturally relevant traits have been primarily developed for use in laboratory and/or greenhouse conditions. In the case of root architectural traits, this limits phenotyping efforts, largely, to young plants grown in specialized containers and growth media. Hence, novel approaches are required to characterize mature root systems of older plants grown under actual soil conditions in the field. Imaging methods able to address the challenges associated with characterizing mature root systems are rare due, in part, to the greater complexity of mature root systems, including the larger size, overlap, and diversity of root components. Our imaging solution combines a field-imaging protocol and algorithmic approach to analyze mature root systems grown in the field. Via two case studies, we demonstrate how image analysis can be utilized to estimate localized root traits that reliably capture heritable architectural diversity as well as environmentally induced architectural variation of both monocot and dicot plants. In the first study, we show that our algorithms and traits (including 13 novel traits inaccessible to manual estimation) can differentiate nine maize (Zea mays) genotypes 8 weeks after planting. The second study focuses on a diversity panel of 188 cowpea (Vigna unguiculata) genotypes to identify which traits are sufficient to differentiate genotypes even when comparing plants whose harvesting date differs up to 14 d. Overall, we find that automatically derived traits can increase both the speed and reproducibility of the trait estimation pipeline under field conditions.
• Bucksch, A., Lindenbergh, R., Abd Rahman, M. Z., & Menenti, M. (2014). Breast Height Diameter Estimation From High-Density Airborne LiDAR Data. IEEE Geoscience and Remote Sensing Letters, 11(6), 1056--1060.
• Bucksch, A., Lindenbergh, R., Rahman, M. Z., & Menenti, M. (2014). Breast Height Diameter Estimation From High-Density Airborne LiDAR Data. IEEE Geoscience and Remote Sensing Letters. doi:10.1109/lgrs.2013.2285471
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  High-density airborne light detection and ranging (LiDAR) data with point densities over 50 points/ m 2 provide new opportunities, because previously inaccessible quantities of an individual tree can be derived directly from the data. We introduce a skeleton measurement methodology to extract the diameter at breast height (DBH) from airborne point clouds of trees. The estimates for the DBH are derived by analyzing the point distances to a suitable tree skeleton. The method is validated in three scenarios: 1) on a synthetic point cloud, simulating the point cloud acquisition over a forest; 2) on examples of free-standing and partly occluded trees; and 3) on automatically extracted trees from a sampled forest. The proposed diameter estimation performed well in all three scenarios, although influences of the tree extraction method and the field validation could not be fully excluded.
• Bucksch, A., Turk, G., & Weitz, J. S. (2014).

  The Fiber Walk: A Model of Tip-Driven Growth with Lateral Expansion

  . PLoS One. doi:10.1371/journal.pone.0085585
• Bucksch, A., Turk, G., & Weitz, J. S. (2014). The fiber walk: a model of tip-driven growth with lateral expansion. PloS one, 9(1), e85585.
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  Tip-driven growth processes underlie the development of many plants. To date, tip-driven growth processes have been modeled as an elongating path or series of segments, without taking into account lateral expansion during elongation. Instead, models of growth often introduce an explicit thickness by expanding the area around the completed elongated path. Modeling expansion in this way can lead to contradictions in the physical plausibility of the resulting surface and to uncertainty about how the object reached certain regions of space. Here, we introduce fiber walks as a self-avoiding random walk model for tip-driven growth processes that includes lateral expansion. In 2D, the fiber walk takes place on a square lattice and the space occupied by the fiber is modeled as a lateral contraction of the lattice. This contraction influences the possible subsequent steps of the fiber walk. The boundary of the area consumed by the contraction is derived as the dual of the lattice faces adjacent to the fiber. We show that fiber walks generate fibers that have well-defined curvatures, and thus enable the identification of the process underlying the occupancy of physical space. Hence, fiber walks provide a base from which to model both the extension and expansion of physical biological objects with finite thickness.
• Das, A., Bucksch, A., Price, C. A., & Weitz, J. S. (2014). ClearedLeavesDB: an online database of cleared plant leaf images. Plant Methods. doi:10.1186/1746-4811-10-8
• Das, A., Bucksch, A., Price, C. A., & Weitz, J. S. (2014). ClearedLeavesDB: an online database of cleared plant leaf images. Plant methods, 10(1), 8.
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  Leaf vein networks are critical to both the structure and function of leaves. A growing body of recent work has linked leaf vein network structure to the physiology, ecology and evolution of land plants. In the process, multiple institutions and individual researchers have assembled collections of cleared leaf specimens in which vascular bundles (veins) are rendered visible. In an effort to facilitate analysis and digitally preserve these specimens, high-resolution images are usually created, either of entire leaves or of magnified leaf subsections. In a few cases, collections of digital images of cleared leaves are available for use online. However, these collections do not share a common platform nor is there a means to digitally archive cleared leaf images held by individual researchers (in addition to those held by institutions). Hence, there is a growing need for a digital archive that enables online viewing, sharing and disseminating of cleared leaf image collections held by both institutions and individual researchers.
• Bucksch, A. (2013). 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. Proceedings of the National Academy of Sciences of the United States of America.
• Bucksch, A. (2013). The Fiber Walk: A Model of Tip-Driven Growth with Lateral Expansion. ArXiv.
• Bucksch, A., & Khoshelham, K. (2013). Localized Registration of Point Clouds of Botanic Trees. IEEE Geoscience and Remote Sensing Letters, 10(3), 631--635.
• Topp, C. N., Iyer-Pascuzzi, A. S., Anderson, J. T., Lee, C. R., Zurek, P. R., Symonova, O., Zheng, Y., Bucksch, A., Mileyko, Y., Galkovskyi, T., Moore, B. T., Harer, J., Edelsbrunner, H., Mitchell-Olds, T., Weitz, J. S., & Benfey, P. N. (2013). 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. Proceedings of the National Academy of Sciences of the United States of America, 110(18), E1695-704.
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  Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r(2) = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.
• Topp, C. N., Iyer‐Pascuzzi, A. S., Anderson, J. T., Lee, C., Zurek, P. R., Symonova, O., Zheng, Y., Bucksch, A., Mileyko, Y., Galkovskyi, T., Moore, B. T., Harer, J., Edelsbrunner, H., Mitchell-Olds, T., Weitz, J. S., & Benfey, P. N. (2013). 3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. Proceedings of the National Academy of Science. doi:10.1073/pnas.1304354110
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  Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r(2) = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.
• Bucksch, A. (2012). GiA Roots: Software for the high throughput analysis of plant root system architecture. BMC Plant Biology.
• Galkovskyi, T., Mileyko, Y., Bucksch, A., Moore, B. T., Symonova, O., Price, C. A., Topp, C. N., Iyer‐Pascuzzi, A. S., Zurek, P. R., Fang, S., Harer, J., Benfey, P. N., & Weitz, J. S. (2012). GiA Roots: software for the high throughput analysis of plant root system architecture. BMC Plant Biology. doi:10.1186/1471-2229-12-116
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  Abstract Background Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks. Results We have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user. Conclusions We demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa . We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.
• Galkovskyi, T., Mileyko, Y., Bucksch, A., Moore, B., Symonova, O., Price, C. A., Topp, C. N., Iyer-Pascuzzi, A. S., Zurek, P. R., Fang, S., Harer, J., Benfey, P. N., & Weitz, J. S. (2012). GiA Roots: software for the high throughput analysis of plant root system architecture. BMC plant biology, 12, 116.
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  Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.
• Bucksch, A., & Fleck, S. (2011). Automated Detection of Branch Dimensions in Woody Skeletons of Fruit Tree Canopies. Photogrammetric Engineering & Remot6e Sensing. doi:10.14358/pers.77.3.229
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  Modeling the 3D canopy structure of trees provides the structural mapping capability on which to assign distributed values of light-driven physiological processes in tree canopies. We evaluate the potential of automatically extracted skeletons from terrestrial lidar data as a basis for modeling canopy structure. The automatic and species independent evaluation method for lidar data of trees is based on the SKELTRE algorithm. The SKELTRE skeleton is a graphical representation of the branch hierarchy. The extraction of the branch hierarchy utilizes a graph splitting procedure to extract the branches from the skeleton. Analyzing the distance between the point cloud points and the skeleton is the key to the branch diameter. Frequency distributions of branch length and diameter were chosen to test the algorithm performance in comparison to manually measured data and resulted in a correlation of up to 0.78 for the branch length and up to 0.99 for the branch diameter.
• Bucksch, A., & Fleck, S. (2011). Automated detection of branch dimensions in woody skeletons of fruit tree canopies. Photogrammetric Engineering \& Remote Sensing, 77(3), 229--240.
• Bucksch, A. (2010). Robust skeleton extraction from imperfect point clouds. Visual Computer.
• Bucksch, A., Lindenbergh, R., & Menenti, M. (2010).

  SkelTre

  . The Visual Computer. doi:10.1007/s00371-010-0520-4
• Bucksch, A., Lindenbergh, R., & Menenti, M. (2010). SkelTre: Robust skeleton extraction from imperfect point clouds. The Visual Computer, 26, 1283--1300.
• Lindenbergh, R. C., & Bucksch, A. K. (2010). Controle afmetingen omega-frames met Tachymetrie.
• Bucksch, A., Lindenbergh, R., Mementi, M., & Raman, M. Z. (2009). Skeleton-based botanic tree diameter estimation from dense LiDAR data. Proceedings Volume 7460, Lidar Remote Sensing for Environmental Monitoring X; 746007 (2009) https://doi.org/10.1117/12.825997. doi:10.1117/12.825997
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  New airborne LiDAR (Light Detection and Ranging) measurement systems, like the FLI-MAP 400 System, make it possible to obtain high density data containing far more information about single objects, like trees, than traditional airborne laser systems. Therefore, it becomes feasible to analyze geometric properties of trees on the individual object level. In this paper a new 3-step strategy is presented to calculate the stem diameter of individual natural trees at 1.3m height, the so-called breast height diameter, which is an important parameter for forest inventory and flooding simulations. Currently, breast height diameter estimates are not obtained from direct measurements, but are derived using species dependent allometric constraints. Our strategy involves three independent steps: 1. Delineation of the individual trees as represented by the LiDAR data, 2. Skeletonization of the single trees, and 3. Determination of the breast height diameter computing the distance of a suited subset of LiDAR points to the local skeleton. The use of a recently developed skeletonization algorithm based on graph-reduction is the key to the breast height measurement. A set of four relevant test cases is presented and validated against hand measurements. It is shown that the new 3-step approach automatically derives breast height diameters deviating only 10% from hand measurements in four test cases. The potential of the introduced method in practice is demonstrated on the fully automatic analysis of a LiDAR data set representing a patch of forest consisting of 49 individual trees.
• Esme, D. L., Bucksch, A., & Beekman, W. H. (2009). Three-dimensional laser imaging as a valuable tool for specifying changes in breast shape after augmentation mammaplasty. Aesthetic plastic surgery, 33(2), 191-5.
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  Three-dimensional (3D) terrestrial laser scanning (TLS) is a valuable method for measuring shapes of objects and for obtaining quantitative measurements. These qualities of the 3D laser scanner have proved to be useful in reconstructive breast surgery. This study investigated various 3D parameters to obtain an optimal objective visualization of the breast after cosmetic augmentation mammaplasty.
• Lindenbergh, R., Uchański, Ł., Bucksch, A., & Gosliga, R. v. (2009).

  STRUCTURAL MONITORING OF TUNNELS USING TERRESTRIAL LASER SCANNING

  . Reports of Geodesy.
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  In recent years terrestrial laser scanning is rapidly evolving as a surveying technique for the monitoring of engineering objects like roof constructions, mines, dams, viaducts and tunnels. The advantage of laser scanning above traditional surveying methods is that it allows for the rapid acquisition of millions of scan points representing the whole surface of the object considered. Still it is a big challenge to obtain accuracies and precisions in the millimeter level when quantifying deformation of an object between epochs. This work presents two major steps towards obtaining sub noise level accuracies in surveying applications using terrestrial laser scan data. The first step aims at obtaining a point cloud of optimal quality for each epoch. The second steps consists of an adjustment and testing procedure that identifies deformation by gaining benefit from both data redundancy and individual point quality. The discussion of both steps is illustrated using several examples from mainly tunnel monitoring projects in the Rotterdam area.
• Rahman, M., Gorte, B., & Bucksch, A. K. (2009). A new method for individual tree delineation and undergrowth removal from high resolution airborne LiDAR. International Archives of Photogrammetry and Remote Sensing, 38(3), 283--288.
• Bucksch, A., & Lindenbergh, R. (2008).

  CAMPINO — A skeletonization method for point cloud processing

  . ISPRS journal of photogrammetry and remote sensing. doi:10.1016/j.isprsjprs.2007.10.004
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  A new algorithm for deriving skeletons and segmentations from point cloud data in O(n) time is explained in this publication. This skeleton is represented as a graph, which can be embedded into the point cloud. The CAMPINO method, (C)ollapsing (A)nd (M)erging (P)rocedures (IN) (O)ctree-graphs, is based on cycle elimination in a graph as derived from an octree based space division procedure. The algorithm is able to extract the skeleton from point clouds generated from either one or multiple viewpoints. The correspondence between the vertices of the graph and the original points of the point cloud is used to derive an initial segmentation of these points. The principle of the algorithm is demonstrated on a synthetic point cloud consisting of 3 connected tori. Initially this algorithm was developed to obtain skeletons from point clouds representing natural trees, measured with the terrestrial laser scanner IMAGER 5003 of Zoller+Fröhlich. The results show that CAMPINO is able to automatically derive realistic skeletons that fit the original point cloud well and are suited as a basis for e.g. further automatic feature extraction or skeleton-based registration.
• Bucksch, A., & Lindenbergh, R. (2008). CAMPINO???A skeletonization method for point cloud processing. ISPRS journal of photogrammetry and remote sensing, 63(1), 115--127.
• Bucksch, A., Gorte, B., & Abd Rahman, M. Z. (2008). FLI-MAP data possibilities for forest inventory.
• Esmé, D. L., Bucksch, A., & Beekman, W. H. (2008).

  Three-Dimensional Laser Imaging as a Valuable Tool for Specifying Changes in Breast Shape After Augmentation Mammaplasty

  . Aesthetic plastic surgery. doi:10.1007/s00266-008-9259-y
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  Three-dimensional (3D) terrestrial laser scanning (TLS) is a valuable method for measuring shapes of objects and for obtaining quantitative measurements. These qualities of the 3D laser scanner have proved to be useful in reconstructive breast surgery. This study investigated various 3D parameters to obtain an optimal objective visualization of the breast after cosmetic augmentation mammaplasty. The objects are represented in a point cloud, which comprises millions of x, y, and z coordinates representing a virtual image. The quantification of 3D points shows changes in height (z coordinate) at any chosen point on the augmented breast (x and y coordinates). To give visual feedback on the change in dimensions, a color elevation scheme was applied on the reconstructed surface of the breast. As a quantifying description, a sagittal B-spline was chosen in a plane through the nipple to obtain the breast shape via the lateral profile. Pre- and postoperative clear images were obtained. The color elevation model showed an increased projection and upper pole fullness after augmentation. The B-spline showed the gain in projection in a sagittal plane through the nipple. Three-dimensional TLS is capable of objectifying changes in shape after augmentation mammaplasty. This imaging technique represents superior visualization of the breast shape and can serve as a valuable tool to determine the changing dimensions of the breasts after augmentation mammaplasty.
• Bucksch, A. K., & Kagkaras, A. (2006). 3D model generation with laser scanners-Approaches towards the improvement of CFD input data and other applications. Leonardo times, 2006(juni), 6--8.

Proceedings Publications

• Bucksch, A. (2025). 3D Buildings modeling Based on a combination of techniques and methodologies.
• Bucksch, A. (2025). A new method for individual tree delineation and undergrowth removal from high resolution airborne LiDAR.
• Bucksch, A. (2025). Applications for point cloud skeletonization in forestry and agriculture. In Reports of Geodesy, Special Issue of the IX Konferencji naukowo-technicznej.
• Bucksch, A. (2025). Automated detection of branch dimensions in woody skeletons of leafless fruit tree canopies.
• Bucksch, A. (2025). Error budget of terrestrial laser scanning: Influence of the incidence angle on the scan quality. In Proceedings 3D-NordOst.
• Binder, S., Hossain, K., Bucksch, A., & Fok, M. (2024). Non-destructive underground fiber Bragg grating sensing system with ResNet prediction for root phenotyping. In Machine Learning in Photonics, 13017.
• Knapp-Wilson, J., Chavez, D. J., & Bucksch, A. (2023, 2023). Analysis of Branch Morphology, Tree Architecture Among Various Peach Cultivars Utilizing TLS Technology. In 2023 ASHS Annual Conference.
• Xie, L., Kim, C., Page, S. H., Kengana, J., Pietrzyk, P., Mcklveen, J., Lavoy, W., Boyd, M. R., Cousins, G., Liu, S., Iersel, M. W., & Bucksch, A. (2023). Indoor-Field: A macro-mesocosm system to study the field dynamics of phenotypic spectrum of common bean (Phaseolus vulgaris. L). In 2023 Meeting of the North American Plant Phenotyping Network.
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  Root studies in controlled environments are typically conducted either in rhizotrons, pots, or small scale mesocosm systems, like PVC tubes or root boxes.These systems have two limitations for translating results to crop roots grown in fields.First, the size and shape of containers change the root phenotype when plants are in the mature stage.Second, often only one plant is planted per container without interaction among neighboring plants.Therefore, the root architecture observed in these isolated environments has low predictability for the root architecture in a community setting in fields.To better translate the root traits observed in a controlled environment to field observations, we developed a macro-mesocosm system (5.5 m (W) x 6.7 m (L) x 0.7 m (H)) to mimic the real field soil conditions in a greenhouse.We also installed 64 capacitance soil moisture sensors to monitor the whole macro-mesocosm system at 15.24 cm and 38.10 cm soil depths in real-time.We evaluated the phenotypic spectrum in one common bean (Phaseolus vulgaris.L) genotype, SEQ7, in a time series experiment.We grew SEQ7 for two, six, nine, and twelve weeks under sensor-controlled water-stressed and well-watered irrigation regimes.SEQ7 showed four different root architecture types across developmental stages.These four root architecture types are consistent with previous field observation.This novel macro-mesocosm system will be a great setup to study the field dynamics of the root phenotypic spectrum in a controlled environment.
• Binder, S., Yang, M., Qiu, V., Bucksch, A., & Fok, M. (2022). Groundwater Level Remote Monitoring Using Optical Power Measurement in Fiber Bragg Grating. In 2022 Optical Fiber Communications Conference and Exhibition (OFC).
• Bucksch, A. (2022). Groundwater Level Remote Monitoring Using Optical Power Measurement in Fiber Bragg Grating. In Optical Fiber Communication Conference (OFC) 2022.
• Bucksch, A. (2022, February). Non-destructive measurements of root traits and their soil-water environment using Fiber Bragg Grating-based fiber optic sensors. In North American Plant Phenotyping Network Conference.
• Bucksch, A. (2022, November). 3D imaging and quantitative analysis of peach tree architecture via TreeQSM. In Acta Horticulturae.
• Knapp-Wilson, J., Bohn Reckziegel, R., Bucksch, A., & Chavez, D. J. (2022). 3D imaging and quantitative analysis of peach tree architecture via TreeQSM. In X International Peach Symposium 1352.
• Knapp-Wilson, J., Reckziegel, R. B., Bucksch, A., & Chavez, D. J. (2022). 3D Phenotyping and Quantitative Analysis of Peach Tree Architecture via Terrestrial Laser Scanning. In HORTSCIENCE, 57.
• Liu, S. B., Paul, B. W., Pietrzyk, P., & Bucksch, A. (2022). Comparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize. In 2022 NAPPN Conference Proceedings.
• Pietrzyk, P., & Bucksch, A. (2022). Improve crop root architecture by resolving self-intersections of individual roots. In 2022 NAPPN Conference Proceedings.
• Xie, L., Burridge, J., Lynch, J., & Bucksch, A. (2022). Phenotypic spectrum: uncovering root architecture diversity in common bean (Phaseolus vulgaris). In Authorea Preprints.
• Xie, L., Kim, C., Page, S., Kengana, J., Pietrzyk, P., Mcklveen, J., Lavoy, W., Boyd, M., Cousins, G., Liu, S., & others, . (2022). Indoor-Field: A macro-mesocosm system to study the field dynamics of phenotypic spectrum of common bean (Phaseolus vulgaris. L). In Authorea Preprints.
• Bucksch, A. (2021, November). 3D phenotyping of peach tree canopy architecture using terrestrial laser scanning. In 2022 NAPPN Meeting.
• Bucksch, A. (2021, October). Tracking dynamic changes of leaves in response to nutrient availability using an open-source cloud-based phenotyping system (OPEN Leaf).
• Kim, C., Xie, L., Bucksch, A., Seymour, L., & Iersel, M. W. (2021). Quantification of Canopy Size Using Automated Chlorophyll Florescence Image Analysis. In 2021 ASHS Annual Conference.
• Knapp-Wilson, J., Reckziegel, R. B., Bucksch, A., & Chavez, D. (2021). Imaging and Quantitative Analysis of Peach Tree Branching Index Via Treeqsm. In HORTSCIENCE, 56.
• Knapp‐Wilson, J., Reckziegel, R. B., Bucksch, A., & Chavez, D. J. (2021).

  3D phenotyping of peach tree canopy architecture using terrestrial laser scanning

  . In proceedings of the 2022 Meeting of the North American Plant Phenotyping Network.
• Liu, S., & Bucksch, A. (2021).

  Comparison of open-source image-based reconstruction pipelines for 3D maize root phenotyping

  . In Proceedings of the 2022 Meeting of the North American Plant Phenotyping Network.
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  Earth and Space Science Open Archive This is a preprint and has not been peer reviewed. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing an older version [v1]Go to new versionComparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maizeAuthorssuxingliuiDWesleyPaul BonelliiDPeterPietrzykiDAlexanderBuckschiDSee all authors suxing liuiDCorresponding Author• Submitting AuthorUniversity of GeorgiaiDhttps://orcid.org/0000-0001-7639-4470view email addressThe email was not providedcopy email addressWesley Paul BonelliiDUniversity of GeorgiaiDhttps://orcid.org/0000-0002-2665-5078view email addressThe email was not providedcopy email addressPeter PietrzykiDUniversity of GeorgiaiDhttps://orcid.org/0000-0002-6794-8133view email addressThe email was not providedcopy email addressAlexander BuckschiDUniversity of GeorgiaiDhttps://orcid.org/0000-0002-1071-5355view email addressThe email was not providedcopy email address
• Swartz, L., Liu, S., Dahlquist, D., Walter, E. S., Mcinturf, S., Bucksch, A., & Mendoza‐Cózatl, D. G. (2021).

  Tracking dynamic changes of leaves in response to nutrient availability using an open-source cloud-based phenotyping system (OPEN Leaf)

  . In Proceedings of the 2022 Meeting of the North American Plant Phenotyping Network.
• Bucksch, A. (2019, September). CAREER: The Phenotypic Spectrum - Quantifying New Patterns Of Architecture Variation In Crop Roots. In NSF PGRP Awardee Meeting.
• Salungyu, J., Kengkanna, J., Thaitad, S., Bucksch, A., & Saengwilai, P. (2018). Root Traits Translation from Laboratory to Field and Their Associations with Drought Tolerance in Maize.. In ASA, CSSA, and CSA International Annual Meeting (2018).
• Bucksch, A. (2016, April). Field phenotyping of root traits in barley. In http://juser.fz-juelich.de/record/809110.
• Wu, Y., Bucksch, A., Scheibe, T., & Bowman, M. M. (2016). Revealing the Hidden Half: Advances in Imaging and Quantification of Plant Roots and Root-Soil Interactions I Posters. In 2016 AGU Fall Meeting.
• Bucksch, A. (2015, October). Root phenotyping of temperate cereals – a high throughput phenotyping pipeline for field experiments. In http://juser.fz-juelich.de/record/280831.
• Wojciechowski, T., Putz, A., Schurr, U., Federau, J., Fiorani, F., Briese, C., Bucksch, A., & Hecht, V. L. (2015). Root phenotyping of temperate cereals--a high throughput phenotyping pipeline for field experiments. In International Society of Root Research Symposium.
• Bucksch, A., & Weitz, J. S. (2014). Computing skeletons of plant networks for a sustainable society. In Gordon Conference on Imaging Science.
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  Poster at the Gordon Conference for Image Science
• Wigington, C., Bucksch, A., & Weitz, J. S. (2014). Towards a unified framework for analyzing bacteria genomes. In School of Biology Retreat.
• Bucksch, A. (2013). Characterizing Tree Growth Anomaly Induced by Landslides Using LiDAR.
• Bucksch, A. (2013). Characterizing tree growth anomaly induced by landslides using lidar. In Landslide Science and Practice: Landslide Inventory and Susceptibility and Hazard Zoning.
• Bucksch, A. (2013). High density airborne lidar estimation of disrupted trees induced by landslides. In International Geoscience and Remote Sensing Symposium (IGARSS).
• Das, A., Bucksch, A., & Weitz, J. S. (2013). Infrastructure for managing and analyzing biological networks derived from collections of plant images. In 1st Workshop on Big Data Research and Development".
• Razak, K. A., Bucksch, A., Straatsma, M., Van, W., Bakar, R. A., & Jong, S. M. (2013). High density airborne LiDAR estimation of distrupted trees induced by landslides. In Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International.
• Das, A., Price, C. A., Bucksch, A., & Weitz, J. S. (2012). SoLID-An Online Community Database of Leaf Images. In SMB Symposium 2012.
• Bucksch, A., & Weitz, J. S. (2011). Canopy parameter extraction from 3D terrestrial laser scan data with SkelTre. In 2nd International Plant Phenotyping Symposium 2011.
• Razak, K. A., Bucksch, A., Damen, M., Westen, C., & Straatsma, M. (2011). Characterizing tree growth anomaly induced by landslides using LiDAR. In Proceedings of the Second World Landslide Forum, 3.
• Bucksch, A. (2010, July). Woody biovolume extraction from laser scanned trees.
• Bucksch, A., Fleck, S., Rumpf, S., & Rademacher, P. (2010). Woody biovolume extraction from laser scanned trees. In Silvilaser 2010.
• Menenti, M., Abd Rahman, M. Z., Bucksch, A. K., Lindenbergh, R. C., & Duong, V. H. (2010). Retrieval of vegatation and surface poperties with terrestrial, airborne and space-borne laser scanners. In 3rd International symposium recent advances in quantitative remote sensing.
• Bucksch, A. (2009). SkelTre - Fast Skeletonisation for Imperfect Point Cloud Data of Botanic Trees.. In 3DOR@Eurographics.
• Bucksch, A. (2009). SkelTre - Fast skeletonisation for imperfect point cloud data of botanic trees. In Eurographics Workshop on 3D Object Retrieval, EG 3DOR.
• Bucksch, A. (2009). Skeleton-based botanic tree diameter estimation from dense LiDAR data. In Proceedings of SPIE - The International Society for Optical Engineering.
• Bucksch, A. (2009, March). Structural monitoring of Tunnels using terrestrial laser scanning. In Reports of Geodesy, Special Issue of the IX Konferencji naukowo-technicznej.
• Bucksch, A. (2009, October). A new method for individual tree measurement from airborne LiDAR.
• Bucksch, A., Lindenbergh, R., & Menenti, M. (2009).

  SkelTre - fast skeletonisation for imperfect point cloud data of botanic trees

  . In 3D Eurographics 2009 (Object Retrieval Workshop).
• Bucksch, A., Lindenbergh, R., & Menenti, M. (2009). Applications for point cloud skeletonizations in forestry and agriculture. In Reports of Geodesy, Special Issue of the IX Konferencji naukowo-technicznej "Aktualne Problemy w Geodezji In\.zynieryjnej???.
• Lindenbergh, R., Uchanski, L., Bucksch, A., & Van, G. R. (2009). Structural monitoring of tunnels using terrestrial laser scanning. In Reports of Geodesy, 2.
• Bucksch, A. (2007). 3D buildings modelling based on a combination of techniques and methodologies. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.
• Bucksch, A. (2007, April). Error budget of terrestrial laserscanning: Influence of the intensity remission of the scan quality. In Proceedings of the III International Scientific Congress Geo-Siberia 2007.
• Bucksch, A. (2007, January). ERROR BUDGET OF TERRESTRIAL LASERSCANNING: INFLUENCE OF THE INTENSITY REMISSION ON THE SCAN QUALITY.
• Bucksch, A., Lindenbergh, R., & Van Ree, J. (2007). Error budget of Terrestrial Laserscanning: Influence of the intensity remission on the scan quality. In GeoSiberia 2007-International Exhibition and Scientific Congress.
• Manea, G., Bucksch, A. K., & Gorte, B. (2007). 3D building modelling based on a combination of techniques and methodologies. In 21st CIPA international symposium" Anticipating the future of cultural past".
• Pop, G., & Bucksch, A. (2007). Combining modern techniques for urban 3D modelling. In 2007 IEEE International Geoscience and Remote Sensing Symposium.
• Pop, G., Bucksch, A., & Gorte, B. (2007).

  3D BUILDINGS MODELLING BASED ON A COMBINATION OF TECHNIQUES AND METHODOLOGIES

  . In XXI International CIPA Symposium, 1-6.
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  Three dimensional architectural models are more and more important for a large number of applications. Specialists look for faster and more precise ways to generate them. This paper discusses methods to combine methodologies for handling data acquired from multiple sources: maps, terrestrial laser and additional measurements, together with digital images for the purpose of generating the 3D visualization of selected buildings. The information used in this kind of projects may have numerous inputs: 3D laser scanning, total stations, digital photogrammetry, laser altimetry, maps - all of them in combination with data generated from other various disciplines and related to the project’s purpose. All 3D data gathering techniques show significant improvements in resolution and accuracy. In this context, aerial and close range photogrammetry, airborne or terrestrial-based laser scanning, mobile mapping and GPS surveying are also on the same trend. A variety of approaches with different resolutions, accuracies, methods, completion times, stages and costs exist. In this paper, an approach towards 3D modelling of buildings and information collection using combined data from digital photogrammetry, mobile mapping, laser scanning, conventional surveying and cartography-based reconstruction, is considered.
• Soudarissanane, S., Van, R. J., Bucksch, A., & Lindenbergh, R. (2007). Error budget of terrestrial laser scanning: influence of the incidence angle on the scan quality. In 3D-NordOst.
• Bucksch, A. (2006). Skeletonization and segmentation of point clouds using octrees and graph theory. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.
• Bucksch, A. (2006, October). Skeletonization and segmentation of point clouds using octrees and graph theory.
• Bucksch, A., & Wageningen, H. (2006). Skeletonization and segmentation of point clouds using octrees and graph theory. In Commission V Symposium, Image Engineering and Vision Metrology.
• Mangoldt, T., Kurth, W., Bucksch, A., & Wernecke, P. (2005). A system for recording 3D information with applications in the measurement of plant structure. In Functional Structural Plant Modelling.
• Mangoldt, T., Kurth, W., Bucksch, A., Wernecke, P., & Diepenbrock, W. (2004). A system for recording 3D information with applications in the measurement of plant structure. In Proceedings of the 4th international workshop on functional-structural plant models: abstracts of papers and posters.
• Bucksch, A. (1996). Treatment With Thyrotropin-Releasing Hormone (TRH) in Patients With Traumatic Spinal Cord Injuries. In Neurology Report.

Presentations

• Bucksch, A. (2024, April). Learning the Shape of Root Architectures. AI webinar of the North American Plant Phenotyping Network. online, world-wide..
• Bucksch, A. (2024, April). Overlooked biology: Plant root phenomena hidden in the noise of agricultural data. 2024 RTG Research Showcase. Tucson, AZ..
• Bucksch, A. (2024, January). Higher yields from degraded land: Advances in quantifying and understanding root-root interactions. Desert Ag Symposium on Soil Health. Yuma, AZ.
• Bucksch, A. (2024, July). Advances in root phenotyping reveal new functional and genetic insights from the cell to the population level. Seminar series of the Center for Sorghum Improvement. Manhatten, KS: Center for Sorghum Improvement.
• Bucksch, A. (2024, June). Imaging and Computational Techniques in Multiscale Vascular Research. Gordon Conference "Plant Vascular Systems". Portland, ME.
• Bucksch, A. (2024, November). Roots: A below-ground plant society. Michigan State University. East Lansing.
• Bucksch, A. (2024, October 2024,). Root phenotyping: Advances in the field. Lincoln, NE. Root Phenotyping Workshop at the 8th International Plant Phenotyping Symposium.
• Bucksch, A. (2024, October). Phenotyping Hair-Like Structures Belowground: Not Every Epidermal Extension is a Root Hair. 8th International Plant Phenotyping Symposium. Lincoln, NE.
• Bucksch, A. (2024, October). Root phenotyping reveals new functional and genetic insights from the population to the cell level. 11th Plant Genomics & Gene Editing Congress. Raleigh, NC, USA..
• Bucksch, A. (2023). The shape of plants to come: Are we really observing so much noise in plant research?. Statistics & Data Science Colloquium.
• Bucksch, A. (2023). Roots don't like competion! - Insights from digging into the noise of agricultural data. School of Plant Science Retreat 2023. Tucson: School of Plant Science.
• Bucksch, A. (2023, August). Analysis of Branch Morphology and Tree Architecture among Various Peach Cultivars utilizing TLS Technology.
• Bucksch, A. (2023, October). What’s that noise? - Unravelling Patterns of Complementary Root Phenotypes. 3rd African Plant Breeders Association (APBA) Conference. Marakesh: African Plant Breeders Association (APBA).
• Bucksch, A. (2022, April). From data to new plants: Advancing computational plant science in the face of climate change.
• Bucksch, A. (2022, March). From data to new plants: Advancing computational plant science in the face of climate change.
• Bucksch, A. (2021, April). Phenotyping the chaos of root shapes at scale.
• Bucksch, A. (2021, August). DIRT and Co. - Enabling discovery in plant biology data.
• Bucksch, A. (2021, February). Making sense of the phenotypic chaos: Techniques for discovery in plant biology data.
• Bucksch, A. (2021, July). Making sense of the phenotypic chaos: Techniques for discovery in plant biology data.
• Bucksch, A. (2021, May). Sorting out the chaos in phenotyping data: From trait measurement to biological discovery.
• Bucksch, A. (2021, November). An algorithm to measure root hair response to abiotic stresses in microscopy images.
• Bucksch, A. (2021, September). DIRT and Co.: Computational discovery in plant biology data.
• Bucksch, A. (2020). Computers, Roots \& Big Data from the Field. ASA, CSSA and SSSA International Annual Meetings (2020)| VIRTUALASA-CSSA-SSSA.
• Bucksch, A. (2020, April). Computers, Roots & Big Data from the Field: Can new methods identify uncharacterized phenomena in existing data?.
• Bucksch, A. (2020, February). Frontiers in root phenotyping: Mathematical and physical limits in the field.
• Bucksch, A. (2020, January). Discovering Uncharacterized Phenotypes in Big Data.
• Bucksch, A. (2020, July). Deterministic spatial modelling in plant biology.
• Bucksch, A. (2020, March). Frontiers in root phenotyping: Mathematical and physical limits in the field.
• Bucksch, A. (2020, May). Variation in root hair traits and responses among plant species, genotypes and root classes to nutrient deficiency.
• Bucksch, A. (2020, November). Computers, Roots & Big Data from the Field.
• Bucksch, A. (2019, April). Frontiers in root phenotyping: Physical and mathematical challenges in the field.
• Bucksch, A. (2019, August). 3D root phenotyping in the field.
• Bucksch, A. (2019, August). Root hair phenotypes of nutrient uptake efficiency in early root development.
• Bucksch, A. (2019, August). The shape of plants revealed - a shape theoretic perspective on statistics of trait measurements.
• Bucksch, A. (2019, December). An algorithm to measure root hair response to abiotic stresses in microscopy images.
• Bucksch, A. (2019, February). Training Computational Plant Scientists: Experiences of student training at the interface.
• Bucksch, A. (2019, March). Frontiers in root phenotyping: Physical and mathematical challenges in the field.
• Bucksch, A. (2019, May). Frontiers in root phenotyping: Physical and mathematical challenges in the field.
• Bucksch, A. (2019, May). Frontiers of root phenotyping in the field.
• Bucksch, A. (2019, November). Frontiers in root phenotyping: Ask anything you never dared to ask to AJ Khwan.
• Bucksch, A. (2019, October). Frontiers in root Physical and mathematical challenges in the field.
• Bucksch, A. (2018, February). Put the carbon back into the soil: 3D root phenotyping for improved carbon sequestration.
• Bucksch, A. (2018, February). The shape of plants to come: Unleashing geometry and topology within the plant sciences.
• Bucksch, A. (2018, January). Unleashing geometry and topology for forestry and agriculture.
• Bucksch, A. (2018, July). Frontiers in root phenotyping under field conditions: Physical and mathematical limits in the light of ongoing developments.
• Bucksch, A. (2018, November). Frontiers in root phenotyping under field conditions: Physical and mathematical limits in the light of ongoing developments.
• Bucksch, A. (2017, April). The shape of plants to come: in situ computation and field math.
• Bucksch, A. (2017, December). The shape of plants to come: Unleashing geometry and topology within the plant sciences.
• Bucksch, A. (2017, December). The shape of plants to come: in situ computation and field math.
• Bucksch, A. (2017, January). Computational advances towards identifying and quantifying in situ plant traits.
• Bucksch, A. (2017, June). Computational advances towards a new characterization of root phenotypes under field conditions.

Poster Presentations

• Bucksch, A. (2023, December). OPEN leaf: an open‐source cloud‐based phenotyping system for tracking dynamic changes at leaf‐specific resolution in Arabidopsis. The Plant Journal.
• Bucksch, A. (2022, November). Novel Analysis of Fruit Tree Architecture and Morphology Via 3D Computational Modeling Indices.
• Bucksch, A. (2022, November). Pheotypic Spectrum: a novel framework to study the root architecture diversity of crop roots at the population level.
• Bucksch, A. (2021, April). Improving the Micronutrient Content of Cassava.
• Bucksch, A. (2021, April). Measuring Root Growth in Soil with Fiber-Bragg Grating Sensors.
• Bucksch, A. (2021, April). Understanding Environmental Parameters' Effects on Common Bean Micronutrient Content.
• Bucksch, A. (2021, August). Imaging and Quantitative Analysis of Peach Tree Architecture via TreeQSM.
• Bucksch, A. (2021, February). DIRT/3D : 3D root phenotyping system for field grown maize.
• Bucksch, A. (2021, February). Discovering Unknown Genes Associated with the Elemental Traits of Root Length and Diameter Using Image Processing Techniques.
• Bucksch, A. (2021, February). Morphological Phenotyping of Hooked Hairs in Phaseolus Vulgaris.
• Bucksch, A. (2021, July). PlantIT: Phenotyping Workflow Automation in the Cloud.
• Bucksch, A. (2021, November). Phenotypic Spectrum: Uncovering Root Architecture Diversity in Common Bean ( Phaseolus vulgaris L.).
• Bucksch, A. (2021, September). Quantification of Canopy Size Using Automated Chlorophyll Florescence Image Analysis. Hortscience.
• Bucksch, A. (2020, April). Application of computer vision and image analysis in 3D bean root phenotyping.
• Bucksch, A. (2020, April). Measuring adaptations of root hairs to phosphorous and nitrogen deficiencies in common bean.
• Bucksch, A. (2020, July). Quantifying diversity of root architecture types within a genotype of common bean (Phaseolus vulgaris. L).
• Bucksch, A. (2020, October). A parameter-free system to measure morphological and physiological traits of Arabidopsis rosettes.
• Bucksch, A. (2020, October). DIRT/3D: 3D phenotyping system for maize architecture in the field.
• Bucksch, A. (2020, September). PlantIT: Workflows on the web, no code necessary..
• Bucksch, A. (2019, August). An algorithm to measure root hair response to abiotic stresses in microscopy images.
• Bucksch, A. (2019, February). 3D root phenotyping for improved carbon sequestration.
• Bucksch, A. (2019, February). Plant IT: An open web platform for advanced plant phenotyping from imaging data.
• Bucksch, A. (2019, July). Root hair phenotypes of nutrient uptake efficiency in early root development of common bean.
• Bucksch, A. (2019, June). Extracting 3D root traits for enhanced carbon sequestration from field and lab data.
• Bucksch, A. (2019, June). The Phenotypic Spectrum: Identifying Whole Root Architecture Types in Genotypes of Common Bean (Phaseolus vulgaris L.).
• Bucksch, A. (2019, March). Effects of Suboptimal Nitrogen, Phosphorus and Potassium on Root Hair Development of Rice, Maize, and Soybean.
• Bucksch, A. (2019, October). An algorithm to measure root hair response to abiotic stresses in microscopy images.
• Bucksch, A. (2019, October). Decoupling SA-based disease resistance and tolerance to abiotic stresses from temperature-dependent growth suppression in Fd-Irp9/COR Arabidopsis.
• Bucksch, A. (2019, October). The shape of plants revealed: A shape theoretic perspective on statistics of trait measurements.
• Bucksch, A. (2019, October). Using DIRT to characterize conidiophore architecture in the model fungus Neurospora crassa.
• Bucksch, A. (2018, February). 3D root phenotyping for improved carbon sequestration.
• Bucksch, A. (2018, February). Automated phenotyping of root hair traits from microscopy images.
• Bucksch, A. (2018, July). Automatic detection and quantification of individual root hairs in complex arrangements.
• Bucksch, A. (2018, July). DEEPER: An integrative platform for deeper roots.
• Bucksch, A. (2018, September). A system to measure adaptation of root hairs to phosphorus and nitrogen deficiencies in common bean (P. vulgaris).
• Bucksch, A. (2018, September). Effects of suboptimal nitrogen, phosphorus and potassium on root hairs development of rice, maize and bean.
• Bucksch, A. (2018, September). Extracting traits from 3D models of maize root system architecture.
• Bucksch, A. (2018, September). Pyramiding SA-based disease resistance, abiotic stress tolerance and growth protection in Arabidopsis.
• Bucksch, A. (2018, September). Replicating field grown root architecture types in the greenhouse.
• Bucksch, A. (2017, July). The phenotypic spectrum of crop roots.
• Bucksch, A. (2017, June). Automated phenotyping of root hair traitsfrom microscopy images.
• Bucksch, A. (2017, June). Hyphal Network Formation in Neurospora crassa.
• Bucksch, A. (2017, June). Linking Root and Shoot: Traits and trade-offs in low nutrient stress tolerance of cultivated sunflower.
• Bucksch, A. (2016, February). Cowpea root architecture has agronomic impact.
• Bucksch, A. (2016, March). Digital Imaging of Root Traits (DIRT) – An online high-throughput phenotyping platform for analyzing root images.
• Bucksch, A. (2016, October). Investigating the molecular, physiological, and architectural changes that underlie grafting-induced vigor.
• Wigington, C., Bucksch, A., & Weitz, J. S. (2015). Towards a unified framework for analyzing bacteria genomes. Georgia Tech School of Biology Retreat.
• Bucksch, A. (2014, June). Computing skeletons of plant networks for a sustainable society.
• Bucksch, A. (2014, November). Phenotypic variation of root architectural, morphological and anatomical traits of Thai rice (Oryza sativa).
• Bucksch, A. (2014, September). Digital Imaging of Root Traits (DIRT) – An online high-throughput phenotyping platform for analysis of root images.
• Bucksch, A. (2014, September). Towards a unified framework for analyzing bacteria genomes.
• Bucksch, A. (2013, April). Infrastructure for managing and analyzing biological networks derived from collections of plant images.
• Bucksch, A. (2012, July). SoLID : An Online Community Database of Leaf Images.
• Bucksch, A. (2011, November). DynBio: An Educational Application to Facilitate the Instruction of Mathematical Modeling in Biology.
• Knipe, K., Bucksch, A., & Weitz, J. S. (2011). DynBio: An Educational Application to Facilitate the Instruction of Mathematical Modeling in Biology. 8th International Conference on Bioinformatics.
• Bucksch, A. (2004, June). A system for recording 3D information with applications in the measurement of plant structure.

Creative Performances

• Bucksch, A. (2025. Farm Monitor: The important role of Ag technology.

Others

• Bucksch, A. (2025). Development of an integrated algorithm for surface reconstruction and point reduction in point clouds.
• Bucksch, A. (2023, December). All Supplement.zip. https://figshare.com/articles/dataset/All_Supplement_zip/24886500
  More info

  Data associated with manuscript "DIRT/µ - Automated extraction of root hair traits using combinatorial optimization"
• Bucksch, A. (2023, January). Three-dimensional phenotyping of peach tree crown architecture utilizing terrestrial laser scanning. https://doi.org/10.22541/essoar.167338515.52669554/v1
• Bucksch, A. (2023, June). CyVerse: Cyberinfrastructure for Open Science. https://doi.org/10.1101/2023.06.16.545223
• Bucksch, A. (2023, October). Analysis of Branch Morphology and Tree Architecture among Various Peach Cultivars utilizing TLS Technology. Hortscience.
• Bucksch, A. (2022, April). Morphological Phenotyping of 'Hooked' Hairs in Phaseolus vulgaris.
• Bucksch, A. (2022, August). Exceeding expectations: the genomic basis of nitrogen utilization efficiency and integrated trait plasticity as avenues to improve nutrient stress tolerance in cultivated sunflower (Helianthus annuus L.). https://doi.org/10.1101/2022.08.28.505579
• Bucksch, A. (2022, February). Cassava root phenotyping for arsenic phytoremediation. https://doi.org/10.1002/essoar.10508363.2
• Bucksch, A. (2022, February). Phenotypic spectrum: uncovering root architecture diversity in common bean (Phaseolus vulgaris).
• Bucksch, A. (2022, February). PlantIT: Containerized phenotyping in the cloud. NAPPN 2022.
• Bucksch, A. (2022, November). Comparison of Open-Source Three-Dimensional Reconstruction Pipelines for Maize-Root Phenotyping. https://doi.org/10.1002/essoar.10512880.1
• Bucksch, A. (2022, November). The soil microbiome reduces Striga infection of sorghum by modulation of host-derived signaling molecules and root development. https://doi.org/10.1101/2022.11.06.515382
• Bucksch, A. (2022, September). 3D Phenotyping and Quantitative Analysis of Peach Tree Architecture via Terrestrial Laser Scanning. Hortscience.
• Bucksch, A. (2021, December). OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis. https://doi.org/10.1101/2021.12.17.472861
• Bucksch, A. (2021, February). Characterization of growth and development of sorghum genotypes with differential susceptibility toStriga hermonthica. https://doi.org/10.1101/2021.02.24.432663
• Bucksch, A. (2021, January). DIRT/3D: 3D phenotyping system for field-grown maize architecture. Plant Physiology.
• Bucksch, A. (2021, November). 3D phenotyping of peach tree canopy architecture using terrestrial laser scanning.
• Bucksch, A. (2021, November). Comparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize.
• Bucksch, A. (2021, November). DIRT/mu: Automatic root hair measurement in maize (Zea mays ssp.) from microscopy images. https://doi.org/10.1002/essoar.10508833.1
• Bucksch, A. (2021, November). Non-destructive measurements of root traits and their soil-water environment using Fiber Bragg Grating-based fiber optic sensors.
• Bucksch, A. (2021, November). Phenotypic Spectrum: Uncovering Root Architecture Diversity in Common Bean ( Phaseolus vulgaris L.). 2021 ASA, CSSA, SSSA International Annual Meeting.
• Bucksch, A. (2021, October). Automatic root hair measurement to quantify abiotic stresses in microscopy images. https://doi.org/10.1002/essoar.10508376.1
• Bucksch, A. (2021, October). Cassava root phenotyping for arsenic phytoremediation. https://doi.org/10.1002/essoar.10508363.1
• Bucksch, A. (2021, October). Comparison of open-source image-based reconstruction pipelines for 3D maize root phenotyping. NAPPN Annual Conference 2022.
• Bucksch, A. (2021, October). Phenotypic spectrum: uncovering root architecture diversity in common bean (Phaseolus vulgaris). https://doi.org/10.1002/essoar.10508344.1
• Bucksch, A. (2021, October). The morphological phenotyping of hooked hairs in Phaseolus Vulgaris.
• Bucksch, A. (2021, September). Imaging and Quantitative Analysis of Peach Tree Branching Index Via Treeqsm. Hortscience.
• Bucksch, A. (2020, February). Keeping your head cool: alleviating temperature stress during drought through root traits in cultivated sunflower (Helianthus annuus). Andries.
• Bucksch, A. (2020, July). DIRT/3D: 3D root phenotyping for field grown maize (Zea mays). https://doi.org/10.1101/2020.06.30.180059
• Bucksch, A. (2020, July). Variation of root hair traits and responses among plant species, genotypes and root classes to nutrient deficiency. Final of the Young Rising Star Scientist Competition.
• Bucksch, A. (2020, May). An algorithm to measure root hair response to abiotic stresses in microscopy images. Proceedings of 2020 Functional Structural Plant Modeling.
• Bucksch, A. (2020, October). PlantIT.
• Bucksch, A. (2019, March). Effects of Suboptimal Nitrogen, Phosphorus and Potassium on Root Hair Development of Rice, Maize, and Soybean. BCT Conference 2018.
• Bucksch, A. (2018, July). Root Traits Translation from Laboratory to Field and Their Associations with Drought Tolerance in Maize. TriSociety Meeting, Baltimore.
• Puttonen, E., Bucksch, A., Zlinszky, A., & Pfeifer, N. (2018). Optical approaches to capture plant dynamics in time, space, and across scales.
• Bucksch, A. (2009, August). Scanning of the Interactive Science Centre Garden.
• Bucksch, A. (2008, August). FLI-MAP data possibilities for forest inventory.
• Bucksch, A. (2006, June). 3D MODEL GENERATION WITH LASER SCANNERS - Approaches towards the improvement of CFD input data. Leonardo Times.