{"title":"基于深度学习的目标检测算法在农田有害生物识别中的应用","authors":"Shi Wenxiu, Li Nianqiang","doi":"10.5121/ijaia.2020.11301","DOIUrl":null,"url":null,"abstract":"Combining with deep learning technology, this paper proposes a method of farmland pest recognition based on target detection algorithm, which realizes the automatic recognition of farmland pest and improves the recognition accuracy. First of all, a labeled farm pest database is established; then uses Faster R-CNN algorithm, the model uses the improved Inception network for testing; finally, the proposed target detection model is trained and tested on the farm pest database, with the average precision up to 90.54%.","PeriodicalId":93188,"journal":{"name":"International journal of artificial intelligence & applications","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5121/ijaia.2020.11301","citationCount":"1","resultStr":"{\"title\":\"Application of Target Detection Algorithm based on Deep Learning in Farmland Pest Recognition\",\"authors\":\"Shi Wenxiu, Li Nianqiang\",\"doi\":\"10.5121/ijaia.2020.11301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Combining with deep learning technology, this paper proposes a method of farmland pest recognition based on target detection algorithm, which realizes the automatic recognition of farmland pest and improves the recognition accuracy. First of all, a labeled farm pest database is established; then uses Faster R-CNN algorithm, the model uses the improved Inception network for testing; finally, the proposed target detection model is trained and tested on the farm pest database, with the average precision up to 90.54%.\",\"PeriodicalId\":93188,\"journal\":{\"name\":\"International journal of artificial intelligence & applications\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.5121/ijaia.2020.11301\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of artificial intelligence & applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5121/ijaia.2020.11301\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of artificial intelligence & applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/ijaia.2020.11301","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Target Detection Algorithm based on Deep Learning in Farmland Pest Recognition
Combining with deep learning technology, this paper proposes a method of farmland pest recognition based on target detection algorithm, which realizes the automatic recognition of farmland pest and improves the recognition accuracy. First of all, a labeled farm pest database is established; then uses Faster R-CNN algorithm, the model uses the improved Inception network for testing; finally, the proposed target detection model is trained and tested on the farm pest database, with the average precision up to 90.54%.