Artificially Ripeness Identification of Indonesian Banana Cultivar Using Convolution Neural Network

Résumé fourni par la source

Detection of bananas that ripen naturally and artificially using digital images is quite difficult because they physically have almost the same characteristics. Feature identification requires very detailed characteristic information so that a diverse feature extraction process is needed, but not necessarily the extracted ones are the optimal features. In this study, the convolution neural network (CNN) method was used and the best features were learned automatically during the classification process. Two CNN-based methods, VGG based model and ResNet18, were tested to obtain the optimal identification. The results of testing on six Indonesian banana cultivars showed that VGG based model produced better accuracy of 88.67%. However, ResNest18 achieved convergence faster.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.