Deep Learning-Based Detection of Apple Leaf Diseases using Image Analysis

In the dynamic field of agriculture, the intelligent diagnosis and classification of plant diseases represent a key research objective. Among widely cultivated fruits, apples are prominent, and diseases such as “Scab” and “Rust” are prevalent in apple plants. Differentiating between these diseases, as well as identifying “multiple diseases” and healthy apple leaves, poses a challenging task. To address this issue, this research proposes an advanced deep learning algorithm capable of accurately classifying and recognizing various apple leaf diseases with minimal loss. Utilizing the ‘Plant Pathology 2020 - FGVC7’ dataset with 1821 images categorized into Scab, Rust, Healthy, and Multiple diseases, our study embarks on an in-depth analysis of apple leaf conditions. Furthermore, the issue of imbalanced datasets is mitigated by implementing the SMOTE algorithm. To determine the optimal model, multiple approaches are explored, including modifying learning rate values, employing different batch sizes, and utilizing diverse optimizer functions. The study encompasses the application of both a simple CNN model and transfer learning models, such as DenseNet, InceptionV3, VGG16, ResNet50, and ResNet101. Through extensive experimentation, it is revealed that the DenseNet model outperforms the others, achieving a remarkable validation accuracy of 97.18%, the findings demonstrate the potential of deep learning algorithms for revolutionizing apple disease detection, offering farmers and agriculturalists a reliable tool to combat plant diseases effectively.