Journal ArticleCharacterizing season‐long floral trajectories in cotton with low‐altitude remote sensing and deep learningAdhikari, Jeevan [CoverCress Inc St Louis Missouri USA] (ORCID:0000000238153067); Petti, Daniel [Department of Agricultural and Biological Engineering The University of Florida Gainesville Florida USA] (ORCID:000000025293445X); Vitrakoti, Deepak [Plant Genome Mapping Laboratory The University of Georgia Athens Georgia USA] (ORCID:000000019009364X); Ployaram, Wiriyanat [Plant Genome Mapping Laboratory The University of Georgia Athens Georgia USA] (ORCID:0000000284294098); Li, Changying [Department of Agricultural and Biological Engineering The University of Florida Gainesville Florida USA] (ORCID:0000000325904797); Paterson, Andrew H [Plant Genome Mapping Laboratory The University of Georgia Athens Georgia USA]<sec><title>Societal Impact Statement</title><p>Plant breeding is a critical tool for increasing the productivity, climate resilience, and sustainability of agriculture, but current phenotyping methods are a bottleneck due to the amount of human labor involved. Here, we demonstrate high‐throughput phenotyping with an unmanned aerial vehicle (UAV) to analyze the season‐long flowering pattern in cotton, subsequently mapping relevant genetic factors underpinning the trait. Season‐long flowering is a complex trait, with implications for adaptation of perennials to specific environments. We believe our approach can improve the speed and efficacy of breeding for a variety of woody perennials.</p></sec> <sec><title>Summary</title><p><list list-type='bullet'><list-item><p>Many perennial plants make important contributions to agroeconomies and agroecosystems but have complex architecture and/or long flowering duration that hinders measurement and selection. Iteratively tracking productivity over a long flowering/fruiting season may permit the identification of genetic factors conferring different reproductive strategies that might be successful in different environments, ranging from rapid early maturation that avoids stresses, to late maturation that utilizes the full seasonal duration to maximize productivity.</p></list-item><list-item><p>In cotton, a perennial plant that is generally cultivated as an annual crop, we apply aerial imagery and deep learning methods to novel and stable genetic stocks, identifying genetic factors influencing the duration and rate of fruiting.</p></list-item><list-item><p>Our phenotyping method was able to identify 24 QTLs that affect flowering behavior in cotton. A total of five of these corresponded to previously identified QTLs from other studies.</p></list-item><list-item><p>While these factors may have different relationships with crop productivity and quality in different environments, their determination adds potentially important information to breeding decisions. With transfer learning of the deep learning models, this approach could be applied widely, potentially improving gains from selection in diverse perennial shrubs and trees essential to sustainable agricultural intensification.</p></list-item></list></p></sec>New Phytologist Foundation2025-11-0110677636PLANTS, PEOPLE, PLANET761657 to 16732572-2611https://doi.org/10.1002/ppp3.106441934481National Science Foundation