{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T02:16:48Z","timestamp":1768011408881,"version":"3.49.0"},"reference-count":25,"publisher":"IGI Global","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,4]]},"abstract":"<jats:p>The automatic classification of crop disease images has important value. The classification algorithm based on manual feature extraction has some problems, such as the need for professional knowledge, is time-consuming and laborious, and has difficulty extracting high-quality features. In this article, the theory of the fuzzy system is discussed. The theory of the fuzzy system is applied to the pretreatment of blurred images. A local blurred image deblurring method based on depth learning is proposed. By training convolutional neural network models with different structures, the image of diseases and insect pests is segmented using normalized segmentation algorithms based on spectral graph theory, and the segmentation knot of leaf diseases is obtained. Finally, the optimal network structure is obtained by comparing the segmentation results with the traditional machine learning algorithm. Experiments show that the segmentation results of pests and diseases obtained by this algorithm have better robustness, generalization, and higher accuracy.<\/jats:p>","DOI":"10.4018\/ijdwm.2020040103","type":"journal-article","created":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T18:34:07Z","timestamp":1580495647000},"page":"34-47","source":"Crossref","is-referenced-by-count":26,"title":["Image Classification of Crop Diseases and Pests Based on Deep Learning and Fuzzy System"],"prefix":"10.4018","volume":"16","author":[{"given":"Tongke","family":"Fan","sequence":"first","affiliation":[{"name":"Xi'an International University, Xi'an, China"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"Xi'an International University, Xi'an, China"}]}],"member":"2432","reference":[{"issue":"01","key":"IJDWM.2020040103-0","first-page":"43","article-title":"Discrimination of the initial stage of citrus canker disease by self-organizing artificial neural network model.","author":"Y.Cai","year":"1995","journal-title":"Journal of Plant Pathology"},{"issue":"6","key":"IJDWM.2020040103-1","first-page":"124","article-title":"Recognition of cucumber anthracnose and brown spot based on color image statistical characteristics.","volume":"34","author":"Z.Cen","year":"2007","journal-title":"Journal of Horticulture"},{"issue":"24","key":"IJDWM.2020040103-2","first-page":"115","article-title":"High speech intelligibility evaluation method based on RMS frequency division.","volume":"54","author":"G.Fei","year":"2018","journal-title":"Computer Engineering and Applications"},{"issue":"1","key":"IJDWM.2020040103-3","first-page":"160","article-title":"A review of content-based image segmentation methods.","volume":"28","author":"J.Feng","year":"2017","journal-title":"Journal of Software"},{"key":"IJDWM.2020040103-4","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"IJDWM.2020040103-5","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"IJDWM.2020040103-6","author":"W.Jia","year":"2012","journal-title":"Prediction of cross-species transmission and antigenic relationship of influenza A virus based on machine learning"},{"key":"IJDWM.2020040103-7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2013.08.003"},{"key":"IJDWM.2020040103-8","unstructured":"Long, J. 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