Identification of apple varieties using hybrid transfer learning and multi-level feature extraction

Abstract

The process of identifying apple varieties holds pivotal importance in pomology and agricultural science. This intricate task not only aids growers in optimizing orchard management, but also profoundly impacts consumers and the apple industry as a whole. Selecting the right apple varieties tailored to specific environmental conditions and market demands is instrumental for the sustainability and economic viability of apple cultivation. Accurate apple variety identification further contributes to maintaining product quality and ensuring consumer satisfaction. Traditional identification methods, however, are susceptible to human error given the vast diversity of apple cultivars. In response, the integration of advanced technologies, including image processing and machine learning, has emerged as a promising approach to enhance accuracy and efficiency in apple variety identification, benefitting both the agricultural and commercial sectors. The classification of apple types involved feature extraction using three methods: MobileNetV2, EfficientNetV2B0, and a combination of GLCM and Color-Space algorithms from apple images. Machine learning models were then built to classify apple varieties, utilizing various algorithms such as support vector machine (SVM), k-nearest neighbors (Knn), random subspace (RSS), and random forest. In the case of "EfficientNetV2B0 + GLCM + Color-Space" and utilizing the ReliefF feature selection method, the random forest algorithm attains peak performance with an accuracy, precision, recall, and F-score all registering an impressive 98.33%.