{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:42:40Z","timestamp":1772779360777,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Modern Agricultural Industrial Technology System Beijing Innovation Team","award":["BAIC08-2025-YJ03"],"award-info":[{"award-number":["BAIC08-2025-YJ03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Insects"],"abstract":"<jats:p>In complex agricultural production environments, small-target pests\u2014characterized by tiny scales, strong background confusion, and close dependence on environmental conditions\u2014pose major challenges to precise monitoring and green pest control. To facilitate the transition from experience-driven to data-driven pest management, a multi-scale vision\u2013sensor collaborative recognition method is proposed for field and protected agriculture scenarios to improve the accuracy and stability of small-target pest recognition under complex conditions. The method jointly models multi-scale visual representations and pest ecological mechanisms: a multi-scale visual feature module enhances fine-grained texture and morphological cues of small targets in deep networks, alleviating feature sparsity and scale mismatch, while environmental sensor data, including temperature, humidity, and illumination, are introduced as priors to modulate visual features and explicitly incorporate ecological constraints into the discrimination process. Stable multimodal fusion and pest category prediction are then achieved through a vision\u2013sensor collaborative discrimination module. Experiments on a multimodal dataset collected from real farmland and greenhouse environments in Linhe District, Bayannur City, Inner Mongolia, demonstrate that the proposed method achieves approximately 93.1% accuracy, 92.0% precision, 91.2% recall, and a 91.6% F1-score on the test set, significantly outperforming traditional machine learning approaches, single-scale deep learning models, and multi-scale vision baselines without environmental priors. Category-level evaluations show balanced performance across multiple small-target pests, including aphids, thrips, whiteflies, leafhoppers, spider mites, and leaf beetles, while ablation studies confirm the critical contributions of multi-scale visual modeling, environmental prior modulation, and vision\u2013sensor collaborative discrimination.<\/jats:p>","DOI":"10.3390\/insects17030281","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:17:31Z","timestamp":1772641051000},"page":"281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Scale Vision\u2013Sensor Collaborative Framework for Small-Target Insect Pest Management"],"prefix":"10.3390","volume":"17","author":[{"given":"Chongyu","family":"Wang","sequence":"first","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yicheng","family":"Chen","sequence":"additional","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangshan","family":"Chen","sequence":"additional","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"},{"name":"National School of Development, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ranran","family":"Chen","sequence":"additional","affiliation":[{"name":"National School of Development, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqi","family":"Xia","sequence":"additional","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruoyu","family":"Hu","sequence":"additional","affiliation":[{"name":"National School of Development, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihong","family":"Song","sequence":"additional","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Swarnkar, S.K., Rathore, Y.K., and Swarnkar, V.K. 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