{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:35:59Z","timestamp":1785411359154,"version":"3.56.0"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Provincial-level College Students\u2019 Entrepreneurship Training Program of Chengdu University","award":["S202511079013S"],"award-info":[{"award-number":["S202511079013S"]}]},{"award":["S202511079013S"],"award-info":[{"award-number":["S202511079013S"]}],"id":[{"id":"https:\/\/ror.org\/034z67559","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tomato leaf diseases pose significant threats to crop yield and food security. However, in real-world cultivation environments, factors such as fluctuating illumination, varying leaf occlusion, and ambiguous lesion morphology often compromise detection accuracy. This paper presents the Gradient-aware Bidirectional Retentive Detection Transformer (GBR-DETR), a model designed for high-precision, real-time disease detection. This model is composed of two network structures and a retentive feature aggregation module: (1) a Multi-scale Gradient-Aware Transfer Network (MGAT-Net) is designed to encode gradient information through the Sobel operator, thereby enhancing the localization stability for small and blurry lesions; (2) a Bidirectional Context Pyramid Network (BCPN) is proposed to enable bidirectional interactions among multi-level features through a top-down and a bottom-up pathway, thereby generating multi-scale lesion features and bridging cross-scale semantic gaps; and (3) a Retentive Feature Aggregation Module (RFAM) is used to suppress background noise and establish global feature correlations, thereby enhancing the overall representation capability for lesion recognition. Experiments on the Multi-scenario Tomato Leaf Disease (M-TLD) dataset show that GBR-DETR yields gains of 3.12, 4.88, and 3.41 percentage points in mAP50\u201395, mAP50, and mAP75, respectively, over the baseline RT-DETR, while also outperforming representative DETR-based and CNN-based detectors. The model demonstrates robust generalization on the PlantDoc cross-domain benchmark, achieving a 2.11% improvement in mAP50 over the baseline. Deployed on the NVIDIA Jetson Orin Nano with TensorRT FP16, it achieves 54 ms latency, enabling real-time disease monitoring on edge devices. This solution provides effective technical support for real-time disease monitoring in smart agriculture.<\/jats:p>","DOI":"10.3390\/s26102950","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T19:10:52Z","timestamp":1778267452000},"page":"2950","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GBR-DETR: A Real-Time Tomato Leaf Disease Detection Model for Edge Device Deployment"],"prefix":"10.3390","volume":"26","author":[{"given":"Jiaxiong","family":"Zhuo","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guikun","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingfeng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feixiong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2885-0117","authenticated-orcid":false,"given":"Xiangjun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"ref_1","unstructured":"Ritchie, H., Rosado, P., and Roser, M. 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