Fruit Ripeness Identification Using YOLOv8 Model

Xiao, Bingjie
Nguyen, Minh
Yan, Wei Qi
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Journal Article
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Springer Science and Business Media LLC

Deep learning-based visual object detection is a fundamental aspect of computer vision. These models not only locate and classify multiple objects within an image, but they also identify bounding boxes. The focus of this paper's research work is to classify fruits as ripe or overripe using digital images. Our proposed model extracts visual features from fruit images and analyzes fruit peel characteristics to predict the fruit's class. We utilize our own datasets to train two "anchor-free" models: YOLOv8 and CenterNet, aiming to produce accurate predictions. The CenterNet network primarily incorporates ResNet-50 and employs the deconvolution module DeConv for feature map upsampling. The final three branches of convolutional neural networks are applied to predict the heatmap. The YOLOv8 model leverages CSP and C2f modules for lightweight processing. After analyzing and comparing the two models, we found that the C2f module of the YOLOv8 model significantly enhances classification results, achieving an impressive accuracy rate of 99.5%.

0801 Artificial Intelligence and Image Processing , 0803 Computer Software , 0805 Distributed Computing , 0806 Information Systems , Artificial Intelligence & Image Processing , Software Engineering , 4009 Electronics, sensors and digital hardware , 4603 Computer vision and multimedia computation , 4605 Data management and data science , 4606 Distributed computing and systems software
Multimedia Tools and Applications, ISSN: 1380-7501 (Print); 1573-7721 (Online), Springer Science and Business Media LLC. doi: 10.1007/s11042-023-16570-9
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