Application of Convolutional Neural Networks for the Classification and Evaluation of Fruit Ripeness

In the present study, we propose an end-to-end trained Convolutional Neural Network (CNN) for classifying and evaluating fruit ripeness, a critical task for modern agriculture that traditionally relies on manual assessments prone to error. Our approach employs advanced Deep Learning techniques to process images of common fruits such as apples, bananas, and oranges, aiming to automate and improve the accuracy of ripeness classification. By comparing it with conventional methods, our model demonstrated a significant improvement, achieving an inference accuracy of 99.2%. The high accuracy rate reflects the model’s ability to minimize false positives and negatives, facilitating more efficient and sustainable management of agricultural resources. These results highlight the potential of Artificial Intelligence technologies to revolutionize agricultural practices. These traditional methods reduce waste and improve food distribution. The implications of this study are particularly relevant for developing technological solutions in the food and agricultural industries, opening new avenues for future research in the automated classification of other farm products.