Advancing Fruit Image Classification with State-of-the-Art Deep Learning Techniques

Abstract

Fruit image classification technology using deep learning is making significant contributions in the agriculture and food retail sectors, promising to increase efficiency and productivity. However, there is an identified knowledge gap in dealing with the considerable variation in fruit appearance caused by factors such as type, size, color, and lighting conditions, as well as the precise identification of damage or disease. This research focuses on applying the developed Convolutional Neural Network architecture to fill this gap by using it in an extensive and diverse dataset, covering 67,692 image files categorized into 131 fruit classes. The training process showed substantial accuracy improvement, with training accuracy reaching 98.39% and validation accuracy at 90%, while training loss decreased to 0.0430 and validation loss to 0.2991. In the advanced stage of training, the training accuracy peaked at 99.43% in the 59th epoch with a shallow loss of 0.0251. However, the validation loss showed variation, indicating room for improvement in model generalization. The findings provide insight into the potential and challenges of applying Convolutional Neural Network models and fruit image classification with improved fruit sorting accuracy. Contribution to the literature in the field of information technology and agriculture by showing deep learning models can be improved to address the issue of fruit image variability.