Mustafa Ahmed, T. Mahajan, Bhupender Datt Sharma, Mahendra Kumar, S. Singh
{"title":"AI-based Detection of Pest Infected Crop and Leaf","authors":"Mustafa Ahmed, T. Mahajan, Bhupender Datt Sharma, Mahendra Kumar, S. Singh","doi":"10.1109/ICSPC51351.2021.9451698","DOIUrl":null,"url":null,"abstract":"Plant infection/disease is one of the ongoing challenges for farmers, which imposes a threat on their income and food security. Detecting infection in plants or crops is an onerous task because the analysis of each crop in large fields takes too much time, effort, work force and expertise. In this paper, we have proposed AI-based detection of pest infected crop and leaf. The proposed method is helpful in analyzing crops in a time-efficient manner and gives more accurate results. The classification of the crops is done on the basis of their images. A publicly available dataset is used to analyze the proposed methodology. Image processing methods are used in order to analyze the crops, further convolutional neural networks are applied to differentiate the healthy crops from the ones that are infected from some disease and also show some visual remarks.","PeriodicalId":182885,"journal":{"name":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd International Conference on Signal Processing and Communication (ICPSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSPC51351.2021.9451698","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
Plant infection/disease is one of the ongoing challenges for farmers, which imposes a threat on their income and food security. Detecting infection in plants or crops is an onerous task because the analysis of each crop in large fields takes too much time, effort, work force and expertise. In this paper, we have proposed AI-based detection of pest infected crop and leaf. The proposed method is helpful in analyzing crops in a time-efficient manner and gives more accurate results. The classification of the crops is done on the basis of their images. A publicly available dataset is used to analyze the proposed methodology. Image processing methods are used in order to analyze the crops, further convolutional neural networks are applied to differentiate the healthy crops from the ones that are infected from some disease and also show some visual remarks.