{"title":"利用图像处理技术检测树叶上昆虫入侵症状","authors":"Muhammad Badrisya Nordin, S. B. Hisham","doi":"10.1109/ICOCO56118.2022.10031739","DOIUrl":null,"url":null,"abstract":"This project aims to help farmers in Lumut, Perak to combat thrips invasion on mango trees. It would help reduce loss of fruit-producing branches, manual inspections, and the need to cover large acres of land manually. Data was collected by using a Canon DSLR camera at lm distance in natural lighting and uncontrolled background. Images of healthy and diseased new leaves are pre-processed to remove noise. Masking and thresholding using a range of intensity values are used to remove the background. After that, the images were clustered using Fuzzy C-Means clustering. It was found that this method was more suitable than K-Means clustering as it uses a soft clustering approach. The images obtained were then classified using Support Vector Machine (SVM). An average classification accuracy of 9S.52% was achieved.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of Insect Invasion Symptoms on Tree Leaves Using Image Processing\",\"authors\":\"Muhammad Badrisya Nordin, S. B. Hisham\",\"doi\":\"10.1109/ICOCO56118.2022.10031739\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This project aims to help farmers in Lumut, Perak to combat thrips invasion on mango trees. It would help reduce loss of fruit-producing branches, manual inspections, and the need to cover large acres of land manually. Data was collected by using a Canon DSLR camera at lm distance in natural lighting and uncontrolled background. Images of healthy and diseased new leaves are pre-processed to remove noise. Masking and thresholding using a range of intensity values are used to remove the background. After that, the images were clustered using Fuzzy C-Means clustering. It was found that this method was more suitable than K-Means clustering as it uses a soft clustering approach. The images obtained were then classified using Support Vector Machine (SVM). An average classification accuracy of 9S.52% was achieved.\",\"PeriodicalId\":319652,\"journal\":{\"name\":\"2022 IEEE International Conference on Computing (ICOCO)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Computing (ICOCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOCO56118.2022.10031739\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Computing (ICOCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOCO56118.2022.10031739","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection of Insect Invasion Symptoms on Tree Leaves Using Image Processing
This project aims to help farmers in Lumut, Perak to combat thrips invasion on mango trees. It would help reduce loss of fruit-producing branches, manual inspections, and the need to cover large acres of land manually. Data was collected by using a Canon DSLR camera at lm distance in natural lighting and uncontrolled background. Images of healthy and diseased new leaves are pre-processed to remove noise. Masking and thresholding using a range of intensity values are used to remove the background. After that, the images were clustered using Fuzzy C-Means clustering. It was found that this method was more suitable than K-Means clustering as it uses a soft clustering approach. The images obtained were then classified using Support Vector Machine (SVM). An average classification accuracy of 9S.52% was achieved.