Leninisha Shanmugam, A. L. A. Adline, N. Aishwarya, G. Krithika
{"title":"利用遥感图像检测作物病害","authors":"Leninisha Shanmugam, A. L. A. Adline, N. Aishwarya, G. Krithika","doi":"10.1109/TIAR.2017.8273696","DOIUrl":null,"url":null,"abstract":"This paper describes an automated diseases detection using remote sensing images. Agriculturists are facing loss due to various crop diseases. It becomes tedious to the cultivators to monitor the crops regularly when the cultivated area is huge (in acres). The most significant part of our research is early detection the disease as soon as it starts spreading on the top layer of the leaves using remote sensing images. This approach has two phases: first phase deals with training of healthy and as well as diseased datasets i.e.) the extraction of threshold values from the image, second phase deals with monitoring of crops and identification of particular disease using canny edge detection algorithm and histogram analysis and also intimate the agriculturists with an early alert message immediately.","PeriodicalId":149469,"journal":{"name":"2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Disease detection in crops using remote sensing images\",\"authors\":\"Leninisha Shanmugam, A. L. A. Adline, N. Aishwarya, G. Krithika\",\"doi\":\"10.1109/TIAR.2017.8273696\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes an automated diseases detection using remote sensing images. Agriculturists are facing loss due to various crop diseases. It becomes tedious to the cultivators to monitor the crops regularly when the cultivated area is huge (in acres). The most significant part of our research is early detection the disease as soon as it starts spreading on the top layer of the leaves using remote sensing images. This approach has two phases: first phase deals with training of healthy and as well as diseased datasets i.e.) the extraction of threshold values from the image, second phase deals with monitoring of crops and identification of particular disease using canny edge detection algorithm and histogram analysis and also intimate the agriculturists with an early alert message immediately.\",\"PeriodicalId\":149469,\"journal\":{\"name\":\"2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TIAR.2017.8273696\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TIAR.2017.8273696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Disease detection in crops using remote sensing images
This paper describes an automated diseases detection using remote sensing images. Agriculturists are facing loss due to various crop diseases. It becomes tedious to the cultivators to monitor the crops regularly when the cultivated area is huge (in acres). The most significant part of our research is early detection the disease as soon as it starts spreading on the top layer of the leaves using remote sensing images. This approach has two phases: first phase deals with training of healthy and as well as diseased datasets i.e.) the extraction of threshold values from the image, second phase deals with monitoring of crops and identification of particular disease using canny edge detection algorithm and histogram analysis and also intimate the agriculturists with an early alert message immediately.