IDENTIFIKASI PENYAKIT CABAI BERDASARKAN GEJALA BERCAK DAUN DAN PENAMPAKAN CONIDIA MENGGUNAKAN PROBABILISTIC NEURAL NETWORK; IDENTIFICATION OF CHILI’S DISEASES BASED ON IT’S LEAF SPOTS AND CONIDIA’S APPEARANCE USING PROBABILISTIC NEURAL NETWORK

PERMADI, JAKA, Agus Harjoko

2016 | Disertasi | FMIPA

Chili is one of the most important commodity in Indonesia which is very susceptible to illness, so the plant’s diseases control early is necessary to preventing the more serious damage. Most of diseases symptoms is visible in their leaves, so by recognizing the initial symptoms from the leaves the plant’s diseases control can be done. The research propose identification of chili’s diseases based on it’s leaf spots and conidia’s appearance from that leaf. Probabilistic neural network is used for recognition and the data that is used in recognition is chili leaf images and microscopic images which is consist of conidias that is extracted from a chili leaf’s surface. Data acquisition is held in Yogyakarta and the result of the process is the conclusion that there is three diseases which is caused by leaf spots in Yogyakarta and two kind of conidia that is extracted from the leaves. The diseases are cercospora leaf spot, bacterial spot and stemphylium grey spot. The two kind of conidias are Cercospora capsici and Leveilula taurica. Identification of diseases is done by two recognition processes, they are spots recognition and conidias recognition. Spots recognition require color features, texture features and shape feature. Those features are ASM_max, ASM-_mean, IDF_max, IDF_mean, contrast_max, contrast_mean, entropy_max, entropy_mean, R_mean, G_mean, B_mean, the difference of entropy GB and circularity ratio. Conidias recognition require shape features (rectangularity, circularity ratio, compactness and eccentricity) and size feature (area of the shape). The accuracy that is obtained from the testing’s result of spots recognition is about 94.737% with the smooth parameter ? = 0.025 and the accuracy that is obtained from the testing’s result of conidias recognition is about 95,313% with the smooth parameter ? = 0.012. The accuracy of diseases identification by using smooth parameters ? for spots and conidias recognition is about 83.06%.

Kata Kunci : PNN, image processing, smooth parameters, features