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Products: Cashews
Subject: Machine learning
Precised Cashew Classification Using Machine Learning
Authors: Karnam, S. N., Vaddagallaiah, V. S., Rangnaik, P. K., Kumar, A., Kumar, C., & Vishwanath, B. M.
- Journals: Eng Technol Appl Sci Res
- Pages: 17414–17421
- Volume: 14(5)
- Year: 2024
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.
https://doi.org/10.48084/etasr.8052
https://doi.org/10.48084/etasr.8052