Pavit Noinongyao, U. Watchareeruetai, Puriwat Khantiviriya, Chaiwat Wattanapaiboonsuk, S. Duangsrisai
{"title":"Separation of abnormal regions on black gram leaves using image analysis","authors":"Pavit Noinongyao, U. Watchareeruetai, Puriwat Khantiviriya, Chaiwat Wattanapaiboonsuk, S. Duangsrisai","doi":"10.1109/JCSSE.2017.8025951","DOIUrl":null,"url":null,"abstract":"This paper proposes an image analysis method for separating abnormal regions caused by nutrient deficiencies on plants' leaves. The proposed method analyzes a histogram of normal leaves' colors to identify abnormalities on leaves. It can be divided into three main steps. Firstly, color features of leaf region in an input image are computed. Secondly, for each pixel, its color features are compared to the corresponding bin in the histogram to determine whether the pixel is abnormal. Finally, a post-processing technique is then applied to reduce noises in the result. Experiments have been conducted using black gram (Vigna mungo) leaves with five different nutrient deficiencies. The experimental results show that the proposed method can separate abnormal regions with an accuracy of above 90%.","PeriodicalId":6460,"journal":{"name":"2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"7 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/JCSSE.2017.8025951","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper proposes an image analysis method for separating abnormal regions caused by nutrient deficiencies on plants' leaves. The proposed method analyzes a histogram of normal leaves' colors to identify abnormalities on leaves. It can be divided into three main steps. Firstly, color features of leaf region in an input image are computed. Secondly, for each pixel, its color features are compared to the corresponding bin in the histogram to determine whether the pixel is abnormal. Finally, a post-processing technique is then applied to reduce noises in the result. Experiments have been conducted using black gram (Vigna mungo) leaves with five different nutrient deficiencies. The experimental results show that the proposed method can separate abnormal regions with an accuracy of above 90%.