Ang Gao, Aijun Geng, Yuepeng Song, Longlong Ren, Yue Zhang, Xiang Han
{"title":"Detection of maize leaf diseases using improved MobileNet V3-small","authors":"Ang Gao, Aijun Geng, Yuepeng Song, Longlong Ren, Yue Zhang, Xiang Han","doi":"10.25165/j.ijabe.20231603.7799","DOIUrl":null,"url":null,"abstract":"In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control, this study proposed a maize disease detection method based on improved MobileNet V3-small, using a UAV to collect maize disease images and establish a maize disease dataset in a complex context, and explored the effects of data expansion and migration learning on model recognition accuracy, recall rate, and F1-score instructive evaluative indexes, and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model. The structured compression of MobileNet V3-small bneck layer retains only 6 layers, the expansion multiplier of each layer was redesigned, 32-fold fast downsampling was used in the first layer, and the location of the SE module was optimized. The improved model had an average accuracy of 79.52% in the test set, a recall of 77.91%, an F1-score of 78.62%, a model size of 2.36 MB, and a single image detection speed of 9.02 ms. The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices. This study provides technical support for realizing the intelligent detection of maize leaf diseases. Keywords: maize leaf disease, image recognition, model compression, MobileNetV3-small DOI: 10.25165/j.ijabe.20231603.7799 Citation: Gao A, Geng A J, Song Y P, Ren L L, Zhang Y, Han X. Detection of maize leaf diseases using improved MobileNet V3-small. Int J Agric & Biol Eng, 2023; 16(3): 225–232.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":2.2000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Agricultural and Biological Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25165/j.ijabe.20231603.7799","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control, this study proposed a maize disease detection method based on improved MobileNet V3-small, using a UAV to collect maize disease images and establish a maize disease dataset in a complex context, and explored the effects of data expansion and migration learning on model recognition accuracy, recall rate, and F1-score instructive evaluative indexes, and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model. The structured compression of MobileNet V3-small bneck layer retains only 6 layers, the expansion multiplier of each layer was redesigned, 32-fold fast downsampling was used in the first layer, and the location of the SE module was optimized. The improved model had an average accuracy of 79.52% in the test set, a recall of 77.91%, an F1-score of 78.62%, a model size of 2.36 MB, and a single image detection speed of 9.02 ms. The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices. This study provides technical support for realizing the intelligent detection of maize leaf diseases. Keywords: maize leaf disease, image recognition, model compression, MobileNetV3-small DOI: 10.25165/j.ijabe.20231603.7799 Citation: Gao A, Geng A J, Song Y P, Ren L L, Zhang Y, Han X. Detection of maize leaf diseases using improved MobileNet V3-small. Int J Agric & Biol Eng, 2023; 16(3): 225–232.
期刊介绍:
International Journal of Agricultural and Biological Engineering (IJABE, https://www.ijabe.org) is a peer reviewed open access international journal. IJABE, started in 2008, is a joint publication co-sponsored by US-based Association of Agricultural, Biological and Food Engineers (AOCABFE) and China-based Chinese Society of Agricultural Engineering (CSAE). The ISSN 1934-6344 and eISSN 1934-6352 numbers for both print and online IJABE have been registered in US. Now, Int. J. Agric. & Biol. Eng (IJABE) is published in both online and print version by Chinese Academy of Agricultural Engineering.