AI-Driven Fruit Classification Using YOLOv7 and SFOA Optimization
Keywords:
Fruit Categorization, YOLO v7, Shuffle Frog Optimization Algorithm (SFOA), Attention Mechanism, Weighted Loss Function, Agricultural Automation, Deep Learning.Abstract
In the evolving landscape of agriculture and food technology, fruit classification plays a crucialrole in enhancing post-harvest quality, minimizing spoilage, and ensuring market readiness.Traditional classification methods often fall short due to high dependency on manual labor,slow training speed, and low accuracy. This study introduces an advanced fruit segmentation
References
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR), 779–788.
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing


