{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:14:29Z","timestamp":1792790069787,"version":"4.5.0"},"reference-count":32,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Finance Science and Technology Project of Changji Hui Autonomous Prefecture","award":["2025S02-03"],"award-info":[{"award-number":["2025S02-03"]}],"id":[{"id":"https:\/\/ror.org\/015bdn754","id-type":"ROR","asserted-by":"crossref"}]},{"award":["2025S02-03"],"award-info":[{"award-number":["2025S02-03"]}],"id":[{"id":"https:\/\/ror.org\/015bdn754","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009110","name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","award":["2026D01A43"],"award-info":[{"award-number":["2026D01A43"]}],"id":[{"id":"10.13039\/100009110","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Joint Fund of the National Natural Science Foundation of China","award":["U24A20159"],"award-info":[{"award-number":["U24A20159"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"award":["U24A20159"],"award-info":[{"award-number":["U24A20159"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recognizing cotton pests and diseases from field-captured images remains challenging due to subtle symptoms, complex backgrounds, high intra-class variation, and considerable variation in symptom scale. This study proposes IFMA-Net, a ResNet-50-based network that integrates local feature enhancement, multi-scale contextual modeling, and interactive feature fusion for agricultural vision sensing. The Dynamic Local Enhancement (DLE) module enhances discriminative shallow features, while the Global Multi-Scale Attention and Local Aggregation (G-MSLA) module combines multi-scale local aggregation with global dependency modeling. The Interactive Cross-Attention Fusion (ICAF) module employs two opposing cross-attention branches to exchange information between local and global representations. On a self-constructed eight-class field dataset, IFMA-Net achieved 95.53 \u00b1 0.17% accuracy and 94.81 \u00b1 0.14% macro-F1, exceeding ResNet-50 by 5.52 and 7.32 percentage points. Ablation experiments confirmed the individual contributions of the three modules. On an independent public test set, IFMA-Net achieved 90.21% macro-F1. Perturbation experiments showed higher retention than ResNet-50 under random occlusion but substantial degradation under severe Gaussian noise. These results demonstrate IFMA-Net\u2019s potential for accurate pest and disease identification in practical agricultural sensing scenarios.<\/jats:p>","DOI":"10.3390\/s26196026","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:09:08Z","timestamp":1790161748000},"page":"6026","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["IFMA-Net: Interactive Feature Modulated Attention Network for Accurate Identification of Cotton Pests and Diseases from Field Images"],"prefix":"10.3390","volume":"26","author":[{"given":"Lili","family":"Song","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250061, China"},{"name":"School of Energy and Control Engineering, Changji University, Changji 831100, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Zhenxue","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250061, China"},{"name":"Xinjiang Research Institute, Shandong University, Changji 831100, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Chengyun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"given":"Yixin","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Dongke","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref_1","first-page":"5761","article-title":"Cotton Leaf Disease Detection Using Vision Transformers: A Deep Learning Approach","volume":"27","author":"Ahmad","year":"2024","journal-title":"Afr. 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