Identification of Pineapple Disease Based on Image Using Neural Network Self-Organizing Map (SOM) Model

Abstract

Pineapple is one of the potential commodities in Indonesia. This is due to high market demand, potential suitable land in Indonesia and public awareness of fruit supply. The main factor of crop failure in pineapple plants is the delay in handling pineapple plant diseases. To identify in image processing requires class grouping. Self-Organizing Map (SOM) which divides the input pattern into several groups so that the network output is in the form of the group that is most similar to the given input. However, the SOM algorithm requires data input that characterizes an object to facilitate the identification process. So, in this study the SOM algorithm was improved through color feature extraction with parameters Red, Green, Blue, Hue, Saturation and Value, as well as texture feature extraction with parameters contrast, correlation, energy, and homogeneity in the Gray Level Co-occurrence Matrix (GLCM). Based on the results of tests carried out by the SOM algorithm with color and texture feature extraction parameters, it is able to assist in increasing the accuracy value. The results of testing the SOM model with color and texture feature extraction obtained a precision value of 93.33%, recall of 92.31% and accuracy of 92.78%.