{"title":"A Smart System for Detection and Classification of Pests Using YOLO AND CNN Techniques","authors":"Sarapu Likith, B. R. Reddy, K. Sripal Reddy","doi":"10.1109/ComPE53109.2021.9752185","DOIUrl":null,"url":null,"abstract":"This paper's central theme is the use of YOLO and CNN to detect and classify pests. The quick extension of the human population opens on to a growth in food requirements. We lose a lot of crops owing to weather conditions and pests because of our country's illiteracy and hardship. Pests wreak havoc on a huge number of crops each and every year. As a result, in order to ensure excellent production in agricultural fields, the pest must be recognized and categorized. Early detection of pests in images is critical for pest reduction and elimination in the agricultural fields. As a result, classification of the Bug present in photographs has been difficult. The major goal of the proposed work is the classification of pests and implement pest- control strategies to safeguard crops from pests. We employ the YOLO (You Only Look Once) algorithm for pest detection and CNN for pest classification (Convolution Neural Network).","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computational Performance Evaluation (ComPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ComPE53109.2021.9752185","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper's central theme is the use of YOLO and CNN to detect and classify pests. The quick extension of the human population opens on to a growth in food requirements. We lose a lot of crops owing to weather conditions and pests because of our country's illiteracy and hardship. Pests wreak havoc on a huge number of crops each and every year. As a result, in order to ensure excellent production in agricultural fields, the pest must be recognized and categorized. Early detection of pests in images is critical for pest reduction and elimination in the agricultural fields. As a result, classification of the Bug present in photographs has been difficult. The major goal of the proposed work is the classification of pests and implement pest- control strategies to safeguard crops from pests. We employ the YOLO (You Only Look Once) algorithm for pest detection and CNN for pest classification (Convolution Neural Network).
本文的中心主题是利用YOLO和CNN对害虫进行检测和分类。人口的迅速增长导致了粮食需求的增长。由于我们国家的文盲和艰苦,天气条件和害虫使我们损失了很多庄稼。害虫每年都对大量农作物造成严重破坏。因此,为了保证农业领域的优质生产,必须对害虫进行识别和分类。图像中害虫的早期发现对于减少和消除农田害虫至关重要。因此,对照片中的虫子进行分类是很困难的。提出的工作的主要目标是害虫的分类和实施害虫防治策略,以保护作物免受害虫的侵害。我们使用YOLO (You Only Look Once)算法进行害虫检测,使用CNN(卷积神经网络)进行害虫分类。