A Raspberry Pi-Guided Device Using an Ensemble Convolutional Neural Network for Quantitative Evaluation of Walnut Quality

Abstract

In this study, a device, augmented by artificial intelligence and controlled by Raspberry Pi, has been engineered for estimating the yield of walnut trees and assessing walnut quality. The device, equipped with a camera, identifies walnuts in real-time using the YOLO V5 detection system. For each detected image of a walnut, feature extraction, selection, and classification were conducted employing a Support Vector Machine (SVM). This methodology facilitated the development of a system capable of determining and recording the quality of all walnuts within a tree or orchard. By leveraging deep neural networks for the analysis of 1800 walnut samples, the device demonstrated an accuracy of 98% in ascertaining walnut quality. This innovative device holds the capacity to swiftly analyze a considerable quantity of walnuts, thereby providing a numerical representation of the quality classes of walnuts cultivated by growers. This quantitative evaluation of walnut quality could subsequently streamline agricultural activities such as irrigation and fertilization, enabling a more efficient and informed approach to these processes. The findings presented in this study underscore the potential of integrating artificial intelligence with practical devices for enhancing the productivity and quality control in agriculture.