Apple Scab Classification Using 2D Shearlet Transform with Integrated Red Deer Optimization Technique in Convolutional Neural Network Models

Apple is an important fruit worldwide, but it is quite susceptible to various diseases. In particular, apple scab disease (Venturia Inaequalis) is a common fungal infection that causes serious yield losses in apple production. This disease causes spots on both leaves and fruits, negatively affecting product quality and marketability. Early diagnosis and management of apple diseases are critical to increase productivity in apple production. Traditional methods are usually time-consuming and costly; therefore, image processing and artificial intelligence technologies have become important tools in disease detection. In this study, a new approach is developed for the classification of healthy and scab apples by combining image processing, deep learning and optimization methods. First, the dataset is enriched using data augmentation techniques such as rotation, mirroring, zooming, shifting, brightness adjustment, and noise addition. Then, the images are analyzed with Shearlet Transform (ST), and frequency and spatial features are extracted in detail. The features obtained from the ST are reconstructed with the inverse transformation, and the original images are given as inputs to deep learning architectures, specifically AlexNet, VGG-16 and ResNet-18. In each model, deep features are extracted to classify healthy and scab apple images, and a feature pool is created by combining these features. The selection process of features that will increase performance in the classification process is carried out with the Red Deer Optimization (RDO) algorithm. This algorithm, inspired by the natural life cycle of male deer, includes the steps of determining the leader deer, creating a harem, mating and selecting the next generations. By selecting the best male leaders and optimizing the mating process, the algorithm ensures that the most effective feature combinations are chosen to enhance classification performance. As a result, this hybrid method presents an innovative approach to accurately classifying healthy and scab apple images, contributing to more efficient and reliable disease detection in apple production.