Brief Summary of Minutes of Annual Meeting

 Purdue University:

• Remote sensing webinar updates (https://ag.purdue.edu/events/department/fnr/2024/08/remote-sensing-in-agricultural-research-webinar-one.html), all webinars are recorded, and it will be a recurring event. One-third of participants were from industry. The major goals were education, extension, and outreach - through enhanced engagement.
• A workshop at North Carolina State Showcase is in planning with Addie Thompson, James Schnable, and Jocab Washburn as speakers.

 University of Kentucky:

• Interest in herbicide damage in early stages (before symptoms developed), especially effects on the photosystem II. Currently using RGB imaging. Potential recommendations included hyperspectral system, LiCOR 800, fluorescence.
• Options to partner with institutes with facilities (Purdue, UNL) were also discussed.

 North Carolina State University:

• Partnership Grant (USDA NIFA AFRI) between UNL and NCSU on the implementation of BRDF techniques has been submitted.
• Potential to include topics such as embedded optical systems (Raspberry Pi for temperature, humidity, optical cameras, etc.) in future curriculum and courses.

 Michigan State University:

• Part of the NSF Research Traineeship (NRT) program developed a certification program in Computational Plant Sciences. There are fundamental classes in computer science and plant science topics and additional specialized classes in each topic. This is a potential course for future course/curriculum development.
• The Horticulture faculty hired to develop outreach and extension program that explores 3D printing for plant science applications.
• Geography courses that explore pipelines for drone data collection and analysis, including drone certification.
• Panel discussion in World Food Prize event on the role of diverse germplasms.
• North American Plant Phenotyping Network (NAPPN) Board decided to have online event (morning) with local hubs (afternoon/evening) that can be hosted by local institute.
• The National Association of Plant Breeders (NAPB) in Hawaii cosponsored by NAPPN will host a phenomics session. In 2026, NAPB will do the same with NAPPN annual meeting.

 Washington State University:

• Ongoing USDA-NIFA Research and Extension Experiences for Undergraduates (REEU) project update and potential new project.
• Currently most of phenomics work in breeding programs at WSU is used in conjunction with genomics selection and assessing spatial variability in field conditions.
• Wheat data and its variability and its associations with phenomics are being evaluated.
• The International Plant Phenotyping Network (IPPN) will have a working group 'Phenomics as a Science' to establish phenomics.

 University of Nebraska Lincoln:

• International Plant Phenotyping Symposium (IPPS) organization has been ongoing.
• There are collaborations explored with the International Maize and Wheat Improvement Center, CIMMYT.
• Ongoing studies on heat stress.
• Discussion on the vision and approaches of commodity boards’ funding priorities across multiple states.

 South Dakota State University

• Sorghum breeding program is being established, in addition to exploring tools to evaluate wheat seeds.
• Exploring identification of genomics regions associated with growth rate with the help of phenomics.
• Development of a robotic system to evaluate structural traits of chili pepper.
• Simulation studies to explore phenomics.

 NC1212 Elicitation by Mike Kudenov: Great discussion on what phenomics means to the participants and what should be the overall goal of this group. The group can bring convergence and be the driver for community to think about this topic. Data repeatability - Can we reproduce reproducible data - the group can answer. Potential opportunities included NSF Global Center among others.

Action Plan:
+ Higher Education Challenge Grant (https://www.nifa.usda.gov/grants/funding-opportunities/higher-education-challenge-grants-program): Sindhuja Sankaran will lead discussion and next steps. To be submitted next year.
+ NSF Global Center, AccelNet: Follow-up discussion on 8 Oct evening through informal meeting. Potential ideas include common sensor/pipeline across location to see if we can produce reproducible data, among others.

Significant updates from the team can be found in the meeting minutes. Additional accomplishments and impacts are detailed below:

 Development of novel technologies and platforms for data acquisition and processing for application in plant phenotyping research.

• Developing phenomics and/or robotic devices for different applications (Sparks, Bao, Sankaran).
• Development of a ground-based mobile robot and multi-view stereo machine vision for high-throughput phenotyping of pod count and flowering phenology to assist breeding of high-yield, heat tolerant lima bean (Bao).

 Development of fundamental methods in data analysis to integrate multi-modal and multi-temporal data for trait prediction.

• Development of an image-based blueberry yield prediction methods using computer vision and machine learning for blueberry breeding (Bao).
• Development of UAV-based and other sensing systems-based data analytics tools (e.g. ML/DL) for phenotyping applications such as waterlogging tolerance in maize (Bao), species identification and coverage in pasture (Revolinski), herbicide injury (Revolinski), barley sprouting (Hirch), Fusarium head blight severity in wheat and barley (Hirch), disease detection in potato tubers (Sankaran), nitrogen status in corn (Yadav), soybean sudden death syndrome detection (Yadav), etc.
• Evaluating impacts of the environment on plant morphology and physiology using optical sensing data and data mining tools (Awada).
• Understanding and modeling of crop water use through multi-scale plant phenotyping and modeling techniques (Bai).

 Exploring the integration of phenomic and genomic models to predict traits.

• Using multi-omics approach to understand drought, temperature, and nutrient stress by integrating high-information phenotyping, ionomics, and global gene expression data to understand the physiological, morphological, elemental, and transcriptomic response of stress across maize seedling development (Hirch).
• Integration of genotypic and phenotypic data of sorghum, winter wheat, and spring wheat germplasms to evaluate GxE interactions and phenotypic plasticity (Mural).

Examples of individual efforts for external competitive funding (funded/pending) to advance this field:

• Accelerated lima bean breeding for climate resiliency using AI-driven high-throughput phenotyping, USDA-NIFA (Bao).
• Enhancing maize resilience to waterlogging; germplasm screening, genetic dissection, and high-throughput phenotyping for climate adaptation, USDA-NIFA (Bao).
• A scalable, low-cost phenotyping strategy for plot and single spike FHB field rating, USDA-U.S. Wheat & Barley Scab Initiative (Hirsch).
• EPSCoR Graduate Research Fellows at Nebraska, NSF EPSCoR Graduate Fellowship Program (EGFP) (Clarke).
• Partnership on data innovation, USDA-ARS (Awada).
• Platform for plant phenomics data management NSF NRI (Awada).

Impacts:

Extension and Outreach Activities:

• Purdue University hosted the 2024 NAPPN Annual Conference.
• Purdue University hosted a webinar series on Remote Sensing Application In Agriculture Research in 2024. It also established a CyVerse account to share the UAV data (RGB, hyperspectral, and LiDAR) from three seasons with colleagues across the world. It is currently constructing a four-season greenhouse that can provide services by the Fall of 2025.
• Several members of this team are a part of the Genomes to Fields initiative.
• Hosting of students from SDSU at UNL (Schnable).
• Active collaboration across institutions on several research activities (Schnable, Ge, Ghosh, Awada, Mural, Thompson).
• Generation and release of a large dataset (6 environments, 100+ hybrids 100,000+ plot images) of UAV and satellite images collected from hybrid yield trials conducted across Nebraska and Iowa (Schnable).
• Acquisition of NSF/IOS Plant Genome Research Program grant to host the 8th International Plant Phenotyping Symposium (Clarke). UNL hosted this symposium. Several members were active participants (Clarke, Ge, Kudenov).
• Clarke was elected as Chair of IPPN, currently serving the Associate Editor of the Plant Phenome journal and is the guest editor of Technical Advances in Plant Science Research Topic: Women in Plant Science - Linking Genome to Phenome, Frontiers in Plant Science.
• Team members are a recipient of Equity, Inclusivity, and Diversity Award from the NAPPN (Clarke).
• Das Choudhury organized a tutorial on HyperProbe Insight: An Interactive Tool for Exploration of Hyperspectral Image Sequences, International Conference on Data Management, Analytics, and Innovation (ICDMAI). She also organized a workshop on Development of Real-time Plant Stress Detection and Quantification Application using Hugging Face, Plant Biology Conference in Hawaii.

 Multistate collaborative efforts for external competitive funding (funded/pending) to advance this field:

• Partnership: Accessible and scalable Galre correction methods for hyper- and multi-spectral imaging using polarized light, USDA-NIFA (Kudenov, Ge, Bai).
• Partnership: Turbocharge center pivots with robotic proximal sensing and AI for precision crop and water management in the Mid-Atlantic, USDA-NIFA (Bao).
• Collaborative Research: Tribal community resilience under climate change: Harnessing controlled environment agriculture to secure sustainability and economic growth – this 4-year grant will support extensive phenotyping cross-state collaborative work with New Mexico and South Dakota, NSF SPSCoR (Guadagno).
• Empowering early stress detection with AI: A global plant phenotyping initiative, Center for Global Studies (Guadagno).
• High-dimensional-high-resolution modeling framework for crop water use efficiency, USDA-NIFA (Bai, Kudenov, Ge)
• Physiological Phenotyping: High-resolution-high-throughput phenotyping pipeline of plant transpiration and soil evaporation for breeding drought-tolerant cultivars, USDA-NIFA (Bai, Ge).

Several involving multi-institutional collaborative work, few examples are underlined:

Ahmed, S., Revolinski, S.R., Maughan, P.W., Savic, M., Kalin, J. and Burke, I.C., 2024. Deep learning–based detection and quantification of weed seed mixtures. Weed Science, pp.1-9. doi:10.1017/wsc.2024.60

Aviles Toledo, C., Crawford, M.M. and Tuinstra, M.R., 2024. Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments. Frontiers in Plant Science, 15, p.1408047. https://doi.org/10.3389/fpls.2024.1408047

Bai, G.F., Barker, B., Scoby, D., Irmak, S., Luck, J.D., Neale, C.M., Schnable, J.C., Awada, T., Kustas, W.P. and Ge, Y., 2024. High-throughput physiological phenotyping of crop evapotranspiration at the plot scale. Field Crops Research, 316, p.109507. doi: 10.1016/j.fcr.2024.109507

Bernád, V., Clarke, J.L. and Negrão, S., 2024. Women in plant science-linking genome to phenome. Frontiers in Plant Science, 15, p.1454686. https://doi.org/10.3389/fpls.2024.1454686

Cassity, M.E., Bartley, P.C. and Bao, Y., 2024. Root segmentation of horticultural plants in X-Ray CT images by integrating 2D instance segmentation with 3D point cloud clustering. Smart Agricultural Technology, 9, p.100666. https://doi.org/10.1016/j.atech.2024.100666

Concepcion, J.S., Noble, A.D., Thompson, A.M., Dong, Y. and Olson, E.L., 2024. Genomic regions influencing the hyperspectral phenome of deoxynivalenol infected wheat. Scientific Reports, 14(1), p.19340. https://doi.org/10.1038/s41598-024-69830-5

Cooper, J., Sweet, D.D., Tirado, S.B., Springer, N.M., Hirsch, C.N. and Hirsch, C.D., 2024. Dissecting the temporal phenomics and genomics of maize canopy cover using UAV mediated image capture. bioRxiv, pp.2024-06. doi: 10.1101/2024.06.25.600603

Das Choudhury, S., Guadagno, C.R., Bashyam, S., Mazis, A., Ewers, B.E., Samal, A. and Awada, T., 2024. Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning. Frontiers in Plant Science, 15, p.1353110. https://doi.org/10.3389/fpls.2024.1353110

Hostetler, A.N., Reneau, J.W., Cristiano, J., Weldekidan, T., Kermani, T.A., Kin, T.T., and Sparts, E.E. 2025. A tool to measure maize root system stiffness that enables a comprehensive understanding of plant mechanics and lodging. Journal of Experimental Botany, erae465. https://doi.org/10.1093/jxb/erae465

Jin, H., Tross, M.C., Tan, R., Newton, L., Mural, R.V., Yang, J., Thompson, A.M. and Schnable, J.C., 2024. Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits. The Plant Phenome Journal, 7(1), p.e20102. https://doi.org/10.1002/ppj2.20102

Jung, J., Fei, S., Tuinstra, M., Yang, Y., Wang, D., Song, C., Gillan, J., Bhandari, M., Ibrahim, A., Zhao, L. and Swetnam, T., 2024, June. Data to science: an open-source online platform for managing, visualizing, and publishing UAS data. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX (Vol. 13053, pp. 12-15). SPIE. https://doi.org/10.1117/12.3021199

Kaiser, E., Von Gillhaussen, P., Clarke, J. and Schurr, U., 2024. IPPS 2022-plant phenotyping for a sustainable future. Frontiers in Plant Science, 15, p.1383766. https://doi.org/10.3389/fpls.2024.1383766

Kim, D., Guadagno, C.R., Ewers, B.E. and Mackay, D.S., 2024. Combining PSII photochemistry and hydraulics improves predictions of photosynthesis and water use from mild to lethal drought. Plant, Cell & Environment, 47(4), pp.1255-1268. https://doi.org/10.1111/pce.14806

Krafft, D., Scarboro, C.G., Hsieh, W., Doherty, C., Balint-Kurti, P. and Kudenov, M., 2024. Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry. Plant Phenomics, 6, p.0157. DOI: 10.34133/plantphenomics.0157

Murphy, K.M., Casto, A.L., Chavez, L., Lima, L.W., Quiñones, A., Gehan, M.A. and Hirsch, C.D., 2024. Maize abiotic stress treatments in controlled environments. Cold Spring Harbor Protocols. 10.1101/pdb.prot108620

Oliveira, M.F., Carneiro, F.M., Ortiz, B.V., Thurmond, M., Oliveira, L.P., Bao, Y., Sanz-Saez, A. and Tedesco, D., 2024. Predicting below and above-ground peanut biomass and maturity using multi-target regression. Computers and Electronics in Agriculture, 218, p.108647. https://doi.org/10.1016/j.compag.2024.108647

Ostermann, I., Benes, B., Gaillard, M., Li, B., Davis, J., Grove, R., Shrestha, N., Tross, M.C. and Schnable, J.C., 2024. Sorghum segmentation and leaf counting using in silico trained deep neural model. The Plant Phenome Journal, 7(1), p.e70002. doi: 10.1002/ppj2.70002

Pal, D., Schaper, K., Thompson, A., Guo, J., Jaiswal, P., Lisle, C., Cooper, L., LeBauer, D., Thessen, A.E. and Ross, A., 2024. Post-GWAS Prioritization of Genome–Phenome Association in Sorghum. Agronomy, 14(12), p.2894. https://doi.org/10.3390/agronomy14122894

Pan, Y., Sun, J., Yu, H., Luck, J., Bai, G., Chamara, N., Ge, Y. and Awada, T., 2024, December. Building Multi-Agent Copilot towards Autonomous Agricultural Data Management and Analysis. In 2024 IEEE International Conference on Big Data (BigData) (pp. 4384-4393). IEEE. doi: 10.1109/BigData62323.2024.10826038.

Panelo, J.S., Bao, Y., Tang, L., Schnable, P.S. and Salas‐Fernandez, M.G., 2024. Genetics of canopy architecture dynamics in photoperiod‐sensitive and photoperiod‐insensitive sorghum. The Plant Phenome Journal, 7(1), p.e20092. https://doi.org/10.1002/ppj2.20092

Powadi, A., Jubery, T.Z., Tross, M.C., Schnable, J.C. and Ganapathysubramanian, B., 2024. Disentangling genotype and environment specific latent features for improved trait prediction using a compositional autoencoder. Frontiers in Plant Science, 15, p.1476070. doi: 10.3389/fpls.2024.1476070

Quiñones, A., Lima, L.W., Murphy, K.M., Casto, A.L., Gehan, M.A. and Hirsch, C.D., 2024. Optimized Methods for Applying and Assessing Heat, Drought, and Nutrient Stress of Maize Seedlings in Controlled Environment Experiments. Cold Spring Harbor Protocols. doi: 10.1101/pdb.top108467

Renó, V., Cardellicchio, A., Romanjenko, B.C. and Guadagno, C.R., 2024. AI-assisted image analysis and physiological validation for progressive drought detection in a diverse panel of Gossypium hirsutum L. Frontiers in Plant Science, 14, p.1305292. https://doi.org/10.3389/fpls.2023.1305292

Rodene, E., Fernando, G.D., Piyush, V., Ge, Y., Schnable, J.C., Ghosh, S. and Yang, J., 2024. Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms. Sensors, 24(7), p.2172. doi: 10.3390/s24072172

Shrestha, N., Mangal, H., Torres‐Rodriguez, J.V., Tross, M.C., Lopez‐Corona, L., Linders, K., Sun, G., Mural, R.V. and Schnable, J.C., 2025. Off‐the‐shelf image analysis models outperform human visual assessment in identifying genes controlling seed color variation in sorghum. The Plant Phenome Journal, 8(1), p.e70013. doi: 10.1101/2024.07.22.604683

Shrestha, N., Powadi, A., Davis, J., Ayanlade, T.T., Liu, H., Tross, M.C., Mathivanan, R.K., Bares, J., Lopez‐Corona, L., Turkus, J., Coffey, L., Tubery, T.Z., Ge, Y., Sarkar, S., Schnable, J.C., Ganapathysubramanian, B., and Schnable, P.S., 2024. Plot‐level satellite imagery can substitute for UAVs in assessing maize phenotypes across multistate field trials. Plants, People, Planet. doi: 10.1002/ppp3.10613

Srivastava, S., Kumar, N., Malakar, A., Das Choudhury, S., Ray, C. and Roy, T., 2024. A Machine Learning-Based Probabilistic Approach for Irrigation Scheduling. Water Resources Management, 38(5), pp.1639-1653. https://doi.org/10.1007/s11269-024-03746-7

Sweet, D., Cooper, J., Hirsch, C.D. and Hirsch, C., 2024. Plant height defined growth curves during vegetative development have the potential to predict end of season maize yield and assist with mid-season management decisions. bioRxiv, pp.2024-06. doi: 10.1101/2024.06.25.600633

Sweet, D.D., Tirado, S.B., Cooper, J., Springer, N.M., Hirsch, C.D. and Hirsch, C.N., 2024. Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits. The Plant Journal, 120(5), pp.1969-1986. doi: 10.1111/tpj.17092

Thomas, H.R., Gevorgyan, A., Hermanson, A., Yanders, S., Erndwein, L., Norman-Ariztía, M., Sparks, E.E. and Frank, M.H., 2024. Graft incompatibility between pepper and tomato elicits an immune response and triggers localized cell death. Horticulture Research, 11(12), p.uhae255. https://doi.org/10.1093/hr/uhae255

Tross, M.C., Grzybowski, M.W., Jubery, T.Z., Grove, R.J., Nishimwe, A.V., Torres‐Rodriguez, J.V., Sun, G., Ganapathysubramanian, B., Ge, Y. and Schnable, J.C., 2024. Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel. The Plant Phenome Journal, 7(1), p.e20106. doi: 10.1002/ppj2.20106

Tuggle, C., Clarke, J.^∗, Murdoch, B., Lyons, E., Scott, N., Benes, B., Campbell, J., Chung, H., Daigle, C., Das Choudhury, S., Dekkers, J., Dorea, J., Ertl, D., Feldman, M, Frangomeni, B., Fulton, J., Guadagno, C., Hagen, D., Hess, A., Kramer, L., Lawrence-Dill, C., Lipka, A., Lubberstedt, T., McCarthy, F., McKay, S., Murray, S., Riggs, P., Rowan, T., Sheehan, M., Steibel, J., Thompson, A., Thornton, K., VanTassell, C., and Schnable, P.  Current challenges and future of agricultural genomes to phenomes in the USA. Genome biology, 25(1), p.8. https://doi.org/10.1186/s13059-023-03155-w

Veloo, K., Carter, A.H., Garland-Campbell, K., Pumphrey, M.O., Rajagopalan, K. and Sankaran, S., 2024. UAV-Derived Digital Trait Analysis for Consistent Representation of Wheat Grain Yield and Adaptability Across Variable Environments. In 2024 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers. doi:10.13031/aim.202401227

Veloo, K., Glenn, A.E., King, A.B., Smith, B.J., Marleau, M.M. and Sankaran, S., 2024. Tuber Ruler: a mobile application for evaluating image-based potato tuber size. Journal of Food Measurement and Characterization, pp.1-10. https://doi.org/10.1007/s11694-024-02542-6

Wei, J., Guo, T., Mu, Q., Alladassi, B.M., Mural, R.V., Boyles, R.E., Hoffmann, L., Hayes, C.M., Sigmon, B., Thompson, A.M. and Salas‐Fernandez, M.G., 2024. Genetic and environmental patterns underlying phenotypic plasticity in flowering time and plant height in sorghum. Plant, Cell & Environment. https://doi.org/10.1111/pce.15213

Ying, S., Webster, B., Gomez-Cano, L., Shivaiah, K.K., Wang, Q., Newton, L., Grotewold, E., Thompson, A. and Lundquist, P.K., 2024. Multiscale physiological responses to nitrogen supplementation of maize hybrids. Plant Physiology, 195(1), pp.879-899. https://doi.org/10.1093/plphys/kiad583

Zarei, A., Li, B., Schnable, J.C., Lyons, E., Pauli, D., Barnard, K. and Benes, B., 2024. PlantSegNet: 3D point cloud instance segmentation of nearby plant organs with identical semantics. Computers and Electronics in Agriculture, 221, p.108922. doi: 10.1016/j.compag.2024.108922

Zheng, R., Jia, Y., Ullagaddi, C., Allen, C., Rausch, K., Singh, V., Schnable, J.C. and Kamruzzaman, M., 2024. Optimizing feature selection with gradient boosting machines in PLS regression for predicting moisture and protein in multi-country corn kernels via NIR spectroscopy. Food Chemistry, p.140062. doi: 10.1016/j.foodchem.2024.140062