Framework for Lightweight Deep Learning Model Using YOLOv5 for Arecanut Grade Assessment

Arecanut grading using Machine Learning (ML) approaches can be less ideal in real-world scenarios that require real-time operations due to the need for rapid processing and adaptation to varying conditions. This paper proposes an efficient and lightweight deep learning model for real-time Arecanut detection suitable for industrial applications. We introduce a novel Arecanut image dataset and explore the YOLOv5 architecture to achieve a lightweight and efficient model for both grading and detection. Our investigation reveals that a hybrid model utilizing GhostNet in Darknet-53 convolutions and a Feature Pyramid Network (FPN) in the YOLOv5 neck structure achieves optimal performance. This model achieves the best mean Average Precision (mAP) of 98.85% while maintaining a compact model size of only 1.9 MB. We compared our design with the latest YOLO models, and ours performed better in detecting Arecanuts. This accurate and cost-effective solution empowers Arecanut producers with the ability to identify top-grade Arecanuts, leading to economic benefits and mitigating health concerns associated with manual grading processes. Finally, we propose a framework for integrating this model into the agro-industry for future real-world implementation.