Pre-trained deep learning-based classification of jujube fruits according to their maturity level

Assessment of crop maturity and quality is pivotal in the food industry and for harvesting. The manual classification of crops based on their maturity levels for harvesting and packaging purpose is a tedious process. However, the emergence of machine learning/deep learning techniques has opened up the ways in this direction, but its practical success is still limited. In this research, we examined two convolutional neural network paradigms (i.e., AlexNet and VGG16) utilizing a transfer-learning approach for classifying the jujube fruits based on their maturity level (i.e., unripe, ripe, and over-ripe). The training/testing of models was performed over the collected dataset of around 400 images, which was further augmented to 4398 images collectively for the three maturity classes. The best accuracy achieved for the correct classification of maturity classes with AlexNet and VGG16 for the actual and augmented images are 94.17% & 97.65%, and 98.26% & 99.17% respectively. The examined models were compared with two existing methods for jujube maturity classification and found to be performing better. The significantly improved success rate of VGG16 models over the AlexNet and existing proposed models for jujube classification makes the model recommendable for developing an efficient system for the automated harvesting and sorting of jujube fruits.