Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean
<|reference_start|>Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean: 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.<|reference_end|>
@article{jones2024multi-sensor,
    title={Multi-Sensor and Multi-temporal High-Throughput Phenotyping for
  Monitoring and Early Detection of Water-Limiting Stress in Soybean},
    author={Sarah E. Jones, Timilehin Ayanlade, Benjamin Fallen, Talukder Z.
  Jubery, Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, and Asheesh
  K. Singh},
    journal={arXiv preprint arXiv:2402.18751},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18751},
    primaryClass={cs.LG cs.CV}
}