Research Project: Sustainable and Resilient Crop Production Systems Based on the Quantification and Modeling of Genetic, Environment, and Management Factors

Location: Adaptive Cropping Systems Laboratory

Publications (Clicking on the reprint icon will take you to the publication reprint.)

Stage-specific drought resilience in cotton revealed by integrating machine learning, physiological traits, spectral phenotyping, and ionomic signatures - (Peer Reviewed Journal)

pdKGraph: A novel approach to constructing plant disease knowledge graphs using large language models - (Peer Reviewed Journal)

The SPAR facility: insights on a legacy with continued relevance for agriculture - (Review Article)
Hassan, M., Beegum, S., Chang, C.Y., Timlin, D.J., Fleisher, D.H., Ray, C., Reddy, V., Reddy, K. 2026. The SPAR facility: insights on a legacy with continued relevance for agriculture. Computers and Electronics in Agriculture. 245. Article 111535. https://doi.org/10.1016/j.compag.2026.111535.

Integrating remote sensing and metabolomics to assess synergistic effects of phosphate deficiency, drought, and AMF symbiosis in soybean - (Peer Reviewed Journal)
Hassan, M.A., Kibbe, R., Muddiman, D.C., Chang, C.Y. 2026. Integrating remote sensing and metabolomics to assess synergistic effects of phosphate deficiency, drought, and AMF symbiosis in soybean. Physiologia Plantarum. https://doi.org/10.1111/ppl.70679.

Inter-comparison of soybean models for simulation of evapotranspiration under rainfed and irrigated conditions - (Peer Reviewed Journal)
Salmeron, M., Kothari, K., Suyker, A., Battisti, R., Boote, K., Archontoulis, S., Constantin, J., Cuadra, S., Debaeke, P., Faye, B., Fleisher, D.H., Grant, B., Hoogenboom, G., Jing, Q., Kimball, B.A., Leung, F., Marin, F., Nendel, C., Qian, B., Schoving, C., Da Silva, E., Smith, W., Srivastava, A., Sun, W., Thorp, K.R., Timlin, D.J., Vieira Jr, N., Williams, K. 2026. Inter-comparison of soybean models for simulation of evapotranspiration under rainfed and irrigated conditions. Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2025.110882.

A dynamic approach for corn yield prediction to ensure agricultural resilience in the U.S. Midwest - (Peer Reviewed Journal)
Mitra, A., Karki, S., Fleisher, D.H., Ray, C., Reddy, V. 2025. A dynamic approach for corn yield prediction to ensure agricultural resilience in the U.S. Midwest. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2025.101295.

TDR-Transformer: A transformer neural network model to determine soil relative permittivity variations along a time domain reflectometry sensor waveguide - (Peer Reviewed Journal)
Wang, Z., Timlin, D.J., Gong, X., Kojima, Y., Hua, S., Fleisher, D.H., Sun, W., Sahila, B., Reddy, V., Tully, K., Horton, R. 2025. TDR-Transformer: A transformer neural network model to determine soil relative permittivity variations along a time domain reflectometry sensor waveguide. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2025.110730.

Intra-specific interactions disrupted the nutrient dynamics and multifactorial responses (drought and salinity) in dalbergia odorifera in a pure planting system - (Peer Reviewed Journal)
Cisse, E.M., Zhou, J., Khan, Y., Miao, L., Fleisher, D.H., Li, D., Tian, M., Yang, F. 2025. Intra-specific interactions disrupted the nutrient dynamics and multifactorial responses (drought and salinity) in dalbergia odorifera in a pure planting system. Tree Physiology. https://doi.org/10.1093/treephys/tpaf097.

Application of a new soil data aggregation method to project irrigation water requirements at a watershed scale - (Peer Reviewed Journal)
Yoo, B., Kim, K., Kim, J., Yu, M., Fleisher, D.H. 2025. Application of a new soil data aggregation method to project irrigation water requirements at a watershed scale. Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2025.126169.

PhenoGazer: A high-throughput phenotyping system to track plant stress responses using hyperspectral reflectance, nighttime chlorophyll fluorescence and RGB imaging in controlled environments - (Peer Reviewed Journal)
Hassan, M.A., Chang, C.Y. 2025. PhenoGazer: A high-throughput phenotyping system to track plant stress responses using hyperspectral reflectance, nighttime chlorophyll fluorescence and RGB imaging in controlled environments. Plant Phenomics. 7(2):100047. https://doi.org/10.1016/j.plaphe.2025.100047.

Sensitivity of sun-induced chlorophyll fluorescence (SIF) and hyperspectral reflectance to drought response in soybean genotypes with contrasting affinities for arbuscular mycorrhizal fungi - (Peer Reviewed Journal)
Chang, C.Y., Barnaby, J.Y., Maul, J.E. 2025. Sensitivity of sun-induced chlorophyll fluorescence (SIF) and hyperspectral reflectance to drought response in soybean genotypes with contrasting affinities for arbuscular mycorrhizal fungi. Remote Sensing of Environment. 323. Article e114722. https://doi.org/10.1016/j.rse.2025.114722.

Assessing fiber quality variability among modern upland cotton cultivars and incorporating it into the GOSSYM-based fiber quality simulation model - (Peer Reviewed Journal)
Beegum, S., Hassan, M., Reddy, K.N., Reddy, V., Reddy, K. 2025. Assessing fiber quality variability among modern upland cotton cultivars and incorporating it into the GOSSYM-based fiber quality simulation model. Journal of Cotton Research. 8. Article e18. https://doi.org/10.1186/s42397-025-00221-5.

Process-based vegetative growth model for cereal rye winter cover crop using object-oriented programming and linked-list data structure - (Peer Reviewed Journal)
Wang, Z., Timlin, D.J., Thapa, R., Fleisher, D.H., Beegum, S., Han, E., Schomberg, H.H., Mirsky, S.B., Sun, W., Reddy, V., Horton, R., Tully, K. 2025. Process-based vegetative growth model for cereal rye winter cover crop using object-oriented programming and linked-list data structure. Computers and Electronics in Agriculture. 231. Article e109964. https://doi.org/10.1016/j.compag.2025.109964.

The crop, land, and soil simulation (CLASSIM) group of models - (Book / Chapter)
Fleisher, D.H., Timlin, D.J., Wang, Z., Beegum, S., Sun, W., Li, S., Barnaby, J.Y., Mitra, A., Yesilkoy, S., Han, E., Reddy, V. 2026. The crop, land, and soil simulation (CLASSIM) group of models” per editor request for publication. Book / Chapter / eBook. https://doi.org/10.19103/AS.2025.0155.02.

Hyperspectral-based high-throughput phenotyping to assess water use efficiency in cotton - (Peer Reviewed Journal)
Beegum, S., Hassan, M., Ramamoorthy, P., Bheemanahalli, R., Reddy, K.N., Reddy, V., Reddy, K. 2024. Hyperspectral-based high-throughput phenotyping to assess water use efficiency in cotton. Journal of Agriculture. 14(7):1054. https://doi.org/10.3390/agriculture14071054.

Planting for perfection: How to maximize cotton quality with the right planting dates in the face of climate change - (Peer Reviewed Journal)
Beegum, S., Raja Reddy, K., Ambinakudige, S., Reddy, V. 2024. Planting for perfection: How to maximize cotton quality with the right planting dates in the face of climate change. Field Crops Research. 315. Article e109483. https://doi.org/10.1016/j.fcr.2024.109483.

Cotton yield prediction: A machine learning approach with field and synthetic data - (Peer Reviewed Journal)
Mitra, A., Beegum, S., Fleisher, D.H., Reddy, V., Sun, W., Ray, C., Timlin, D.J., Malakar, A. 2024. Cotton yield prediction: A machine learning approach with field and synthetic data. IEEE Access. 12:101273-101288. https://doi.org/10.1109/access.2024.3418139.