Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean

• URL: http://arxiv.org/abs/2402.18751v1
• Date: Wed, 28 Feb 2024 23:18:15 GMT
• Title: Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean
• Authors: Sarah E. Jones, Timilehin Ayanlade, Benjamin Fallen, Talukder Z. Jubery, Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, and Asheesh K. Singh
• Abstract summary: Water limiting stress, i.e. drought, emerges as a significant risk for soybean production. This project combines multi-modal information to identify the most effective and efficient automated methods to investigate drought response.
• Score: 5.373434048230662
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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