Deep Learning Based System for of Grading Cashew Kernels

Abstract

Cashew nuts are cultivated widely across the globe and are categorized into various classes according to their size and quality. Currently, cashew kernels are sorted and graded manually, which is time-consuming and labor-intensive. This research proposes hybrid models to automate and accelerate the cashew classification process by integrating classifiers with deep convolutional neural networks (DCNNs). The proposed hybrid models categorize cashew kernels into five groups: W180, W210, W300, W400, and W500. Various hybrid models, including RCNN + OpenCV, were implemented and evaluated based on specificity, accuracy, and other metrics. The results demonstrated that the RCNN model paired with OpenCV achieved a max- imum accuracy of 90%. This study’s findings highlight that automating cashew grading can be significantly enhanced by combining DCNNs with classifiers in hybrid models. These hybrid models improve efficiency by enabling automatic, effective, and precise classification of cashews. By leveraging advanced machine learning techniques, the process becomes faster, more accurate, and less dependent on manual labor. Overall, the integration of deep learning technologies with traditional classifiers offers a promising approach to modernizing and optimizing the cashew grading industry. Future research could further refine these hybrid models to achieve even higher accuracy and efficiency, potentially revolutionizing agricultural sorting processes beyond cashew nuts. Index Terms—DCNN, OpenCV, RCNN, Hybrid Models