Determination of Various Diseases in Two Most Consumed Fruits using Artificial Neural Networks and Deep Learning Techniques

This study investigates the detection of diseases in apples and quinces using artificial neural networks and deep learning techniques to minimize economic losses in fruit production. It developed a real-time image recognition system using two convolutional neural network architectures, achieving an accuracy of 83.3% with the proposed architecture compared to 81.3% with the AlexNet model. The dataset included images of 22 apples and 18 quinces, aimed at improving disease identification and aiding producers in their agricultural practices.