{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T20:06:16Z","timestamp":1791317176994,"version":"4.3.1"},"reference-count":91,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012009","name":"United Soybean Board","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>\n                    Drought is the major abiotic stress limiting soybean growth and yield, yet accurately identifying genotypes that sustain yield under rainfed conditions remains a major bottleneck in soybean breeding. Canopy wilting scores are widely used as a proxy for evaluating plant responses to drought stress. However, most assessments rely on leaf-level visual observations that are inherently subjective and typically based on single time-point scores, providing only a snapshot of stress expression and failing to capture their relationship with yield retention under rainfed conditions. To address these limitations, this study used Unmanned Aerial Vehicle (UAV)-based high-throughput phenotyping at a single growth stage (R4\/R5) as a more quantitative and objective alternative to visual scoring, with closer relevance to yield performance under drought conditions. From 2023 to 2025, a total of 85 soybean genotypes developed by soybean breeding programs in Arkansas, Missouri, Kansas, and North Carolina, along with commercial checks, were evaluated under irrigated and rainfed conditions in Stuttgart, Arkansas. Visual canopy wilting scores were recorded at R4\/R5, along with vegetation indices captured using UAV-based multispectral imagery. UAV-derived indices showed significant correlations with yield (\n                    <jats:italic>r<\/jats:italic>\n                    \u00a0=\u00a00.22 to 0.45, p&amp;lt;0.05) under rainfed conditions. In contrast, visual canopy wilting scores displayed weak and inconsistent associations with yield (\n                    <jats:italic>r<\/jats:italic>\n                    \u00a0=\u00a0-0.28 to 0.35, p&amp;lt;0.05), suggesting limited ability to capture yield retention under rainfed conditions. Unsupervised\n                    <jats:italic>k<\/jats:italic>\n                    -means clustering (\n                    <jats:italic>n<\/jats:italic>\n                    \u00a0=\u00a02) of UAV-derived vegetation indices separated genotypes into two distinct canopy response groups that were consistent across 2023 to 2025 rainfed seasons. Significant differences were observed among clusters for several vegetation indices (ARI, CIG, CIRE, GSAVI, GNDVI, GOSAVI, OSAVI, NDVI), indicating contrasting canopy stress responses. Under rainfed conditions, these UAV-defined clusters also differed for grain yield (2023: 1,925.6\n                    <jats:italic>vs<\/jats:italic>\n                    1,703.1 kg\/ha; 2024: 1,849.9\n                    <jats:italic>vs<\/jats:italic>\n                    1,229.2 kg\/ha; 2025: 2,056.7\n                    <jats:italic>vs<\/jats:italic>\n                    1,773.8 kg\/ha), whereas visual wilting scores failed to distinguish yield-retaining genotypes. Overall, UAV-based high-throughput phenotyping offers a robust and yield-relevant alternative to visual wilting scores, supporting the development of drought-tolerant soybean germplasm and cultivars.\n                  <\/jats:p>","DOI":"10.3389\/fpls.2026.1835549","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T05:42:27Z","timestamp":1780378947000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["UAV-based phenotyping outperforms visual canopy wilting for evaluating soybean drought tolerance and yield retention under rainfed conditions"],"prefix":"10.3389","volume":"17","author":[{"given":"Popat Shivaji","family":"Pawar","sequence":"first","affiliation":[{"name":"Department of Crop, Soil, and Environmental Sciences, University of Arkansas","place":["Fayetteville, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengjun","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Crop, Soil, and Environmental Sciences, University of Arkansas","place":["Fayetteville, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derrick","family":"Harrison","sequence":"additional","affiliation":[{"name":"Department of Crop, Soil, and Environmental Sciences, University of Arkansas","place":["Fayetteville, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Rogers","sequence":"additional","affiliation":[{"name":"Department of Crop, Soil, and Environmental Sciences, University of Arkansas","place":["Fayetteville, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Fallen","sequence":"additional","affiliation":[{"name":"Nitrogen Fixation Unit, United States Department of Agriculture (USDA)-Agriculture Research Service","place":["Raleigh, NC, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caio","family":"Canella Vieira","sequence":"additional","affiliation":[{"name":"Department of Crop, Soil, and Environmental Sciences, University of Arkansas","place":["Fayetteville, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rsase.2022.100782","article-title":"Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in West Africa","volume":"27","author":"Alabi","year":"2022","journal-title":"Remote Sens. 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