{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T03:43:47Z","timestamp":1775187827137,"version":"3.50.1"},"reference-count":166,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Excellent Youth Foundation of Xinjiang Uygur Autonomous Region of China","award":["2023D01E01"],"award-info":[{"award-number":["2023D01E01"]}]},{"name":"Outstanding Young Talent Foundation of Xinjiang Uygur Autonomous Region of China","award":["2023TSYCCX0043"],"award-info":[{"award-number":["2023TSYCCX0043"]}]},{"name":"Tianshan Innovation Team Program of Xinjiang Uygur Autonomous Region of China","award":["2023D14012"],"award-info":[{"award-number":["2023D14012"]}]},{"name":"Finance science and technology project of Xinjiang Uygur Autonomous Region","award":["2023B01029-1"],"award-info":[{"award-number":["2023B01029-1"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62266043"],"award-info":[{"award-number":["62266043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Major crops worldwide are affected by various diseases yearly, leading to crop losses in different regions. The primary methods for addressing crop disease losses include manual inspection and chemical control. However, traditional manual inspection methods are time-consuming, labor-intensive, and require specialized knowledge. The preemptive use of chemicals also poses a risk of soil pollution, which may cause irreversible damage. With the advancement of computer hardware, photographic technology, and artificial intelligence, crop disease recognition methods based on spectral and red\u2013green\u2013blue (RGB) images not only recognize diseases without damaging the crops but also offer high accuracy and speed of recognition, essentially solving the problems associated with manual inspection and chemical control. This paper summarizes the research on disease recognition methods based on spectral and RGB images, with the literature spanning from 2020 through early 2025. Unlike previous surveys, this paper reviews recent advances involving emerging paradigms such as State Space Models (e.g., Mamba) and Generative AI in the context of crop disease recognition. In addition, it introduces public datasets and commonly used evaluation metrics for crop disease identification. Finally, the paper discusses potential issues and solutions encountered during research, including the use of diffusion models for data augmentation. Hopefully, this survey will help readers understand the current methods and effectiveness of crop disease detection, inspiring the development of more effective methods to assist farmers in identifying crop diseases.<\/jats:p>","DOI":"10.3390\/jimaging12020066","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T16:30:47Z","timestamp":1770309047000},"page":"66","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2178-6552","authenticated-orcid":false,"given":"Haoze","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"}]},{"given":"Heran","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"}]},{"given":"Hualong","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6564-4745","authenticated-orcid":false,"given":"Yurong","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"},{"name":"Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830091, China"},{"name":"Xinjiang Engineering Research Center of Big Data and Intelligent Software, School of Software, Xinjiang University, Urumqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1002\/aepp.13335","article-title":"War in Ukraine: The rational \u201cwait-and-see\u201d mode of global food markets","volume":"45","author":"Legrand","year":"2023","journal-title":"Appl. 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