Theodora Sanida, Dimitris Tsiktsiris, Argyrios Sideris, M. Dasygenis
{"title":"植物病害分类的异构轻量级网络","authors":"Theodora Sanida, Dimitris Tsiktsiris, Argyrios Sideris, M. Dasygenis","doi":"10.1109/MOCAST52088.2021.9493415","DOIUrl":null,"url":null,"abstract":"The economy of many countries worldwide depends on the agricultural sector. In agriculture, plant cultivation is often affected by diseases. The plant diseases can cause partial or complete damage to the leaves and fruit. This leads to significant economic loss in the agricultural industry. At the same time, plant diseases, if not detected in an early stage, have negative consequences on the quantity, quality and production of agricultural crops. Thus, the biggest challenge in agriculture is the accurate and early diagnosis and the prevention of plant diseases. In this study, we modified the structure of a popular classification network, the MobileNet V2 to improve the overall accuracy of it, by applying the transfer learning technique and fine-tuning. Experimental findings have shown that the Modified MobileNet V2 architecture achieves a high level of overall accuracy compared to other similar CNN lightweight architectures used in plant leaf disease detection in the recent literature.","PeriodicalId":146990,"journal":{"name":"2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A Heterogeneous Lightweight Network for Plant Disease Classification\",\"authors\":\"Theodora Sanida, Dimitris Tsiktsiris, Argyrios Sideris, M. Dasygenis\",\"doi\":\"10.1109/MOCAST52088.2021.9493415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The economy of many countries worldwide depends on the agricultural sector. In agriculture, plant cultivation is often affected by diseases. The plant diseases can cause partial or complete damage to the leaves and fruit. This leads to significant economic loss in the agricultural industry. At the same time, plant diseases, if not detected in an early stage, have negative consequences on the quantity, quality and production of agricultural crops. Thus, the biggest challenge in agriculture is the accurate and early diagnosis and the prevention of plant diseases. In this study, we modified the structure of a popular classification network, the MobileNet V2 to improve the overall accuracy of it, by applying the transfer learning technique and fine-tuning. Experimental findings have shown that the Modified MobileNet V2 architecture achieves a high level of overall accuracy compared to other similar CNN lightweight architectures used in plant leaf disease detection in the recent literature.\",\"PeriodicalId\":146990,\"journal\":{\"name\":\"2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST)\",\"volume\":\"110 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MOCAST52088.2021.9493415\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MOCAST52088.2021.9493415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Heterogeneous Lightweight Network for Plant Disease Classification
The economy of many countries worldwide depends on the agricultural sector. In agriculture, plant cultivation is often affected by diseases. The plant diseases can cause partial or complete damage to the leaves and fruit. This leads to significant economic loss in the agricultural industry. At the same time, plant diseases, if not detected in an early stage, have negative consequences on the quantity, quality and production of agricultural crops. Thus, the biggest challenge in agriculture is the accurate and early diagnosis and the prevention of plant diseases. In this study, we modified the structure of a popular classification network, the MobileNet V2 to improve the overall accuracy of it, by applying the transfer learning technique and fine-tuning. Experimental findings have shown that the Modified MobileNet V2 architecture achieves a high level of overall accuracy compared to other similar CNN lightweight architectures used in plant leaf disease detection in the recent literature.