{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:45:53Z","timestamp":1784997953207,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["XJJC2024081"],"award-info":[{"award-number":["XJJC2024081"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>With the rapid development of precision agriculture and smart farming management, accurate crop disease detection has become a critical tool for optimizing agricultural resource allocation, controlling operational costs, and supporting scientific plant protection strategies. However, real-world field environments are often characterized by strong background interference, multiple concurrent diseases, and fine-grained lesion differences, posing significant challenges to existing detection methods in practical agricultural Internet of Things (IoT) applications. In this paper, we propose Freq-spatial Context Dynamic Network(FCDNet), an efficient and cost-effective detection model tailored for multi-category strawberry disease recognition in complex field management scenarios. The proposed model integrates a Freq-Spatial Feature Module (FSFM), a Context Guide Fusion Module (CGFM), and a Task Align Dynamic Detection Head (TADDH), enabling enhanced expression of high-frequency micro-lesions, adaptive filtering of field background noise, and spatial alignment of classification and regression tasks, while maintaining a lightweight architecture suitable for low-cost agricultural edge devices. Extensive experiments conducted on the newly constructed Strawberry Disease Dataset-7(S7DD) demonstrate that FCDNet consistently outperforms existing mainstream methods, achieving an F1-score of 91.0% and an mAP@0.5 of 94.6%. The model\u2019s architectural robustness and capacity for generalization are further substantiated by evaluations across diverse agricultural datasets using PlantDoc and ALDOD. Ultimately, FCDNet became a practical and cost-effective tool for real-time detection of strawberry diseases, directly supporting more accurate yield forecasting and risk management in smart agriculture systems.<\/jats:p>","DOI":"10.3390\/plants15091341","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T11:33:37Z","timestamp":1777376017000},"page":"1341","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FCDNet: An Efficient and Cost-Effective Strawberry Disease Detection Model for Smart Farming Management"],"prefix":"10.3390","volume":"15","author":[{"given":"Ruoyu","family":"Ouyang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China"},{"name":"School of Business, Wuxi Taihu University, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junying","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Business, Wuxi Taihu University, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yujia","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Business, Wuxi Taihu University, Wuxi 214122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8826-8279","authenticated-orcid":false,"given":"Jialei","family":"Zhan","sequence":"additional","affiliation":[{"name":"College of System Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of System Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6148","DOI":"10.1073\/pnas.1707462114","article-title":"Smart farming is key to developing sustainable agriculture","volume":"114","author":"Walter","year":"2017","journal-title":"Proc. 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