M. Sravanthi, Dr. Bhaludra R Nadh Singh, Ms. Badalgama Sandhya Rani, Ms. ChandaSharanya, Ms. Vulupala, Sri Varsha
{"title":"Forecast Plant life using Artificial Intelligence","authors":"M. Sravanthi, Dr. Bhaludra R Nadh Singh, Ms. Badalgama Sandhya Rani, Ms. ChandaSharanya, Ms. Vulupala, Sri Varsha","doi":"10.1109/ACCAI58221.2023.10199519","DOIUrl":null,"url":null,"abstract":"India is primarily an agricultural economy, hence accurate diagnosis of plant diseases is crucial to minimizing economic losses. Spending millions of rupees every year to safeguard crops from a wide range of diseases is only possible because of the outdated methods of plant disease diagnosis. Human detection of plant disease is imperfect at best. There is no guarantee of an accurate outcome, even with the help of experts in plant diseases, the time, effort, and knowledge required to diagnose the precise illness. Machine learning and image processing are two such technologies that have proven effective in this regard. By analysing plant photos captured by cameras, we show how machine learning may be applied to a specific issue in plant disease diagnosis.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACCAI58221.2023.10199519","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
India is primarily an agricultural economy, hence accurate diagnosis of plant diseases is crucial to minimizing economic losses. Spending millions of rupees every year to safeguard crops from a wide range of diseases is only possible because of the outdated methods of plant disease diagnosis. Human detection of plant disease is imperfect at best. There is no guarantee of an accurate outcome, even with the help of experts in plant diseases, the time, effort, and knowledge required to diagnose the precise illness. Machine learning and image processing are two such technologies that have proven effective in this regard. By analysing plant photos captured by cameras, we show how machine learning may be applied to a specific issue in plant disease diagnosis.