Pests and Diseases Identification in Mango using MATLAB

Abstract

This study provides assistance to mango farmers in Pampanga by identifying the pests and diseases through leaf and fruit markings in enhancing crop management specifically in pesticide application, addressing one of the main factors of the major decline in Philippines' mango production, which is due to pests and the farmer's uncertainty in using pesticide for each occurrence of pest. Anthracnose, fruit borer and sooty mold were identified using image processing employing multi-SVM and GLCM with 85% accuracy. It was determined by extracting contrast, kurtosis, skewness, and entropy. This research project can be used as a template for other fruit-bearing trees and a basis for crop management per location-based specifically on mango farming using data science.