@inproceedings{scholars17266,
             doi = {10.1109/ICOCO56118.2022.10031739},
            year = {2022},
            note = {cited By 0; Conference of 2022 IEEE International Conference on Computing, ICOCO 2022 ; Conference Date: 14 November 2022 Through 16 November 2022; Conference Code:186566},
           pages = {141--144},
           title = {Detection of Insect Invasion Symptoms on Tree Leaves Using Image Processing},
         journal = {2022 IEEE International Conference on Computing, ICOCO 2022},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
          author = {Nordin, M. B. and Hisham, S. B. B.},
            isbn = {9781665489966},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148476989&doi=10.1109\%2fICOCO56118.2022.10031739&partnerID=40&md5=0ef62535c91f585c163d1d9be9a3bcc3},
        keywords = {Fruits; Fuzzy logic; Image processing; Support vector machines, Fuzzy logic clustering; Images processing; Mango trees; Manual inspection; Natural lighting; Producing branches; REmove noise; Support vectors machine; Thrip invasion); Tree leaf, K-means clustering},
        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. {\^A}{\copyright} 2022 IEEE.}
}