Precised Cashew Classification Using Machine Learning

This study explores the utilization of deep learning techniques for accurate cashew classification to improve efficiency and accuracy in the cashew industry. YOLOv5, YOLOv9, and a Convolutional Neural Network (CNN) were evaluated in classifying cashews into whole, broken, split-up, split-down, and defect categories. A comprehensive labeled dataset was built to train the models, applying data augmentation to increase robustness. YOLOv5 achieved the highest accuracy of 97.65% and the fastest inference time (0.025 s per image) among the models, making it suitable for real-time applications. Although CNN offered a simpler architecture, YOLOv5's superior performance places it as the most promising candidate for large-scale cashew classification deployment.