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Publication Type : Conference Paper
Publisher : IEEE
Source : 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 2023, pp. 663-668
Url : https://ieeexplore.ieee.org/document/10290326
Campus : Chennai
School : School of Engineering
Department : Electronics and Communication
Year : 2023
Abstract : Efficiently managing supply chains, reducing food waste, and ensuring product quality in consumer goods relies significantly on the precise grading of banana maturity. This task is complex due to the subtle morphological and textural changes occurring during ripening. Addressing this challenge, this research introduces a deep YOLOv8 neural network approach for classifying bananas into six categories: fresh-ripe, fresh-unripe, overripe, ripe, rotten, and unripe. The study involves training and evaluating five YOLOv8 models—YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x—using a dataset of 18,074 images. The models achieved detection accuracy ranging from 94.6% (YOLOv8n) to 96.3% (YOLOv8x) for mean average precision (mAP) with an Intersection of Union (IoU) of 0.5. Notably, YOLOv8s displayed strong potential for real-time fruit ripeness classification. For single image predictions, the estimated processing time on Nvidia Jetson Xavier AGX varied from 13.8ms (YOLOv8n) to 230.4ms (YOLOv8x), respectively.
Cite this Research Publication : Aishwarya N and Vinesh Kumar R, "Banana Ripeness Classification with Deep CNN on NVIDIA Jetson Xavier AGX," 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Kirtipur, Nepal, 2023, pp. 663-668