AI-Driven Fruit Classification Using YOLOv7 and SFOA Optimization

Authors

  • Narendra Chennupati

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

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Published

2023-12-21

How to Cite

Narendra Chennupati. (2023). AI-Driven Fruit Classification Using YOLOv7 and SFOA Optimization. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1251–1260. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2549

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Section

Articles