ArticlePlant phenomics (Washington, D.C.)2025
Development of an automated phenotyping platform and identification of a novel QTL for drought tolerance in soybean.
Article in Plant phenomics (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- SoyCountNet: a deep learning framework for counting and locating soybean seeds in field environment.Frontiers in plant science · 2026Article
- Identifying canopy wilting QTLs and evaluating remote sensing approaches for selecting drought-tolerant soybean.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
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4 authors.
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Abstract
Deep understanding of slow-wilting is essential for developing drought-tolerant crops. Existing approaches to measure transpiration rates are difficult to apply to large populations due to their high cost and low throughput. To overcome these challenges, we developed a high-throughput phenotyping system that integrates a load cell sensor and an Arduino-based microcontroller device. The system tracked the transpiration rate in real time by measuring changes in the pot weight in 224 recombinant inbred lines of Taekwangkong (fast-wilting) x SS2-2 (slow-wilting) under water-restricted conditions. Among five transpiration features we determined, stress recognition time point (SRTP) and decrease in transpiration rate by stress (DTrs) are informative parameters, that are interconnected and independently affect slow-wilting as well. Quantitative trait loci (QTL) for SRTP and DTrs were identified at the same location as the major QTL for slow wilting,
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