Deep learning-based approach for classifying mandarin orange maturity

Abstract

The precise prediction of fruit maturity is essential for determining the optimal harvest time. It helps to reduce postharvest losses and maintain consistent fruit quality for consumers. Traditional methods for assessing maturity depend largely on manual inspection. This process is subjective, time-consuming, and prone to human error. Deep learning approaches, particularly convolutional neural networks (CNNs), offer a promising alternative by automating classification with high precision and consistency. This research seeks to identify the most effective deep-learning algorithms for predicting the maturity of mandarin oranges. In this study, the performance of four convolutional neural network architectures (EfficientNet-B0, ResNet50, VGG16, and a Custom CNN) was investigated for the classification of mandarin oranges based on their maturity levels: unripe, ripe, and overripe. The primary dataset comprised 1095 images, with each category containing 365 images. The deep learning models achieved the best accuracy rates of 98% for both EfficientNet-B0 and ResNet50, 83% for VGG16, and an impressive 99% for the Custom CNN, considering primary images. By comparing these models on a balanced dataset, this work offers a practical guide for researchers and practitioners on selecting models for assessing fruit maturity. Notably, EfficientNet-B0, ResNet50, and the Custom CNN exhibited significantly higher success rates compared to VGG16 and existing models, making them particularly recommendable for the development of an efficient automated system for harvesting and sorting mandarin oranges in the near future. The results aim to identify potential applications for improving agricultural practices, quality assessment, and overall efficiency in the food industry.