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arXiv Machine Learning By Md. Mehedi Hasan Naeem, Ishtiyak Ahmmad Araf, Md. Arefin Haque Mahir, Tushar Kanti Saha

An Efficient Approach to Beetle Damage Detection of Banana Using Deep Learning

Read the original on arXiv Machine Learning →

The paper presents a lightweight YOLOv8n-based deep learning framework for detecting beetle damage on banana fruits by classifying visible surface conditions as healthy or unhealthy. Using a custom dataset of 2,473 images from Bangladesh, the model achieved 81.36% precision, 74.30% recall, and 77.67% F1-score on a test set, with a 5.96 MB checkpoint and 6.54 ms per image inference on an NVIDIA Tesla T4. The results demonstrate that YOLOv8n can serve as a compact baseline for instance-level banana surface inspection under the studied conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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