Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean
Date
2024-02-28
Authors
Jones, Sarah E.
Ayanlade, Timilehin
Fallen, Benjamin
Jubery, Talukder Z.
Singh, Arti
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arXiv
Abstract
Soybean production is susceptible to biotic and abiotic stresses, exacerbated by extreme weather events. Water limiting stress, i.e. drought, emerges as a significant risk for soybean production, underscoring the need for advancements in stress monitoring for crop breeding and production. This project combines multi-modal information to identify the most effective and efficient automated methods to investigate drought response. We investigated a set of diverse soybean accessions using multiple sensors in a time series high-throughput phenotyping manner to: (1) develop a pipeline for rapid classification of soybean drought stress symptoms, and (2) investigate methods for early detection of drought stress. We utilized high-throughput time-series phenotyping using UAVs and sensors in conjunction with machine learning (ML) analytics, which offered a swift and efficient means of phenotyping. The red-edge and green bands were most effective to classify canopy wilting stress. The Red-Edge Chlorophyll Vegetation Index (RECI) successfully differentiated susceptible and tolerant soybean accessions prior to visual symptom development. We report pre-visual detection of soybean wilting using a combination of different vegetation indices. These results can contribute to early stress detection methodologies and rapid classification of drought responses in screening nurseries for breeding and production applications.
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Multi-sensor and multi-temporal high-throughput phenotyping for monitoring and early detection of water-limiting stress in soybean
(Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America,
2024-11-30)
Soybean (Glycine max [L.] Merr.) production is susceptible to biotic and abiotic stresses, exacerbated by extreme weather events. Water limiting stress, that is, drought, emerges as a significant risk for soybean production, underscoring the need for advancements in stress monitoring for crop breeding and production. This project combined multi-modal information to identify the most effective and efficient automated methods to study drought response. We investigated a set of diverse soybean accessions using multiple sensors in a time series high-throughput phenotyping manner to: (1) develop a pipeline for rapid classification of soybean drought stress symptoms, and (2) investigate methods for early detection of drought stress. We utilized high-throughput time-series phenotyping using unmanned aerial vehicles and sensors in conjunction with machine learning analytics, which offered a swift and efficient means of phenotyping. The visible bands were most effective in classifying the severity of canopy wilting stress after symptom emergence. Non-visual bands in the near-infrared region and short-wave infrared region contribute to the differentiation of susceptible and tolerant soybean accessions prior to visual symptom development. We report pre-visual detection of soybean wilting using a combination of different vegetation indices and spectral bands, especially in the red-edge. These results can contribute to early stress detection methodologies and rapid classification of drought responses for breeding and production applications.
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Comments
This is a preprint from Jones, Sarah E., Timilehin Ayanlade, Benjamin Fallen, Talukder Z. Jubery, Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, and Asheesh K. Singh. "Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean." arXiv preprint arXiv:2402.18751 (2024). doi: https://doi.org/10.48550/arXiv.2402.18751. Copyright 2024 The Authors.