Identifikasi Citra Hama Tomat Menggunakan Gray Level Co-occurrence Matrix dan Klasifikasi Probabilistic Neural Network

Abstract

Pests cause a major failures in harvesting tomato plants. Identification of tomato pests can be done in various ways. Nowadays, objects can be performed by processing digital images. In this research, Gray Level Co-occurrence Matrix (GLCM) is used to identify three classes of plant pests of tomato, namely Helicoverpa armigera, Spodoptera litura and Chrysodeixis chalcites. For identification, only three types of pests in adults phase was used. Identification is conducted using the five elements of grayscale image: energy, homogeneity, contrast, correlation and entropy. The identification result using Probabilistic Neural Network (PNN) produces average accuracy of 78.89%.