Net Type Melon Fruit Grade Evaluation System with Artificial Neural Network and Fuzzy Logic Preprocess

Abstract

This research utilize Artificial Neural Network (ANN), image processing and fuzzy logic as preprocess in evaluating melon fruit grade system. There are 66 samples out of 80 samples for ANN input with fuzzy logic in taste data normalization phase that suitable for organoleptic test. 49 unit image for image processing based on melon fruit characteristic were use for ANN input. Mutilayer neural network with 1 hidden layer and back propagation algorithm were use in ANN architecture for training, validation and prediction phases. The optimal ANN architecture were use 30 hidden layer node with 0.8 learning rate. The result are 90% total accuratecy in validation process consist of 100% prediction accuratecy of sweetness, 75% medium taste and 100% sour taste.