Fruit Quality Classification using Convolutional Neural Network

Abstract

Fruit quality identification is very important in the food industry for maintaining product quality. The quality control in the food industry commonly conducted by human senses which is lack of objectivity and takes long time for real-time mass production quality control. The quality of the fruit can be identified through its color, smell, and texture. This study uses fruit image to classify the quality of the fruit. We trained artificial neural networks for classifying fruit quality from Indian Fruit Dataset with Quality (FruitNet). The dataset contains six classes of fruits with three categorical qualities (Good, Bad, and Mixed). The dataset features were extracted using several pre-trained deep learning networks trained on the ImageNet dataset. The convolutional networks for feature extraction used in this study are VGG16, MobileNetV2, EfficientNetB0, and ResNet50. The extracted features are forwarded to neural network for training the dataset. The result shown that f1-score for testing dataset reaches more than 90% except for MobileNetV2. The highest f1-score is obtained from ResNet50 feature extraction which is 95.7%.