Wavelet scattering transform and deep features for automated classification and grading of dates fruit

Abstract

Date fruit, a vital agricultural product in the Middle East area, is harvested annually in millions of metric tons and is renowned for its abundant nutrients. With computer vision and machine learning techniques, automatic date fruit classification enables farmers and supermarkets to differentiate between various varieties and qualities of date fruits within their inventory. Date fruits have unique physical characteristics, such as shape, size, color, texture, and skin type that are important in determining their variety and quality. These characteristics can vary significantly depending on the cultivar, growing conditions, and ripening stage of the date fruits. This paper presents a novel date fruit type classification and grading system achieved through the feature-level fusion of deep learning features and wavelet scattering features. Wavelet scattering features are extracted at varying levels of decomposition; enabling reliable extraction of information from diverse channels. To extract deep features this study utilizes pre-trained architectures, including Alexnet, Googlenet, Resnet, and MobileNetV2. The proposed methodology has been experimentally evaluated with the Date Fruit in Controlled Environment dataset, which has nine classes, and has yielded an accuracy of 95.9% for date species classification. Various date fruit species from the TU-DG dataset were graded, and for Ajwa species, the accuracy is 97.8%, for Mabroom, 92.6% accuracy, and for Sukkary, 99.5% accuracy.