{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T20:42:23Z","timestamp":1791837743643,"version":"4.3.4"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cotton Verticillium wilt resistance grading under field conditions remains challenging because disease symptoms are spatially heterogeneous within plots, local pathological cues are unevenly distributed across high-resolution unmanned aerial vehicle (UAV) orthomosaics, and disease-related responses are not expressed with the same sensitivity or spatial consistency across RGB, raw multispectral (MS), and vegetation-index (VI) representations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address these challenges, this study proposes TPAC-Net, a UAV-based Tri-source Pathological Alignment and Coupling Network for plot-level cotton Verticillium wilt resistance grading, which organizes red\u2013green\u2013blue (RGB) imagery, raw MS imagery, and VI representations derived from MS bands as complementary representation streams of visible canopy structure, band-level spectral responses, and index-enhanced disease-sensitive spectral contrasts. Based on field survey records and the relative disease index (RDI) defined by GB\/T 22101.5-2009, two-class and five-class classification settings were constructed, with the standardized five-class task serving as the primary benchmark. TPAC-Net combines source-specific encoders, a Tri-Source Feature Attention Module (TFAM) for adaptive pathological feature coupling, a Cross-Source Semantic Alignment Module (CSAM) for high-level semantic consistency among complementary representations, and a Local Pathogenic Patch Perception strategy for plot-level decision-making from local patches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed framework achieved F1-scores of 79.20% and 50.37% under the two-class and five-class settings, respectively. Under the primary five-class task, it yielded an absolute F1-score gain of 14.36 percentage points over the best-performing baseline. Localized-response visualization showed that higher model responses were concentrated in selected local canopy regions, although fine-grained discrimination among intermediate resistance grades remained challenging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These results suggest that multi-source UAV remote sensing can support field-survey-derived RDI resistance-grade inference through structured representation alignment, pathological feature coupling, and local evidence aggregation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpls.2026.1911947","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T05:33:24Z","timestamp":1788759204000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-source UAV remote sensing for cotton Verticillium wilt resistance 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