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- W4312355877 abstract "In Horticulture, an automatic fruit plucking systems may significantly cut labor expenses while improving picking efficiency. Currently, an autonomous fruit plucking system depends heavily on vision intelligence to identify fruit from the background. Most known approaches are being tested under steady environmental conditions, where the light illumination is relatively perpetual, and the fruits are unimpeded. However, the large-scale implementation of autonomous fruit picking systems is hampered because of the increased ramification of the surrounding environment, including fluctuations in illumination and pervasive occlusion. We propose a fruit recognition technique based on finetuned YOLOv5s to address fruit plucking challenges. The proposed technique detects Custard Apple, Pomegranate, and Waxberry fruits. First, fruit images from a real-world setting were gathered and preprocessed to create a dataset. The model is then trained on the dataset to precisely recognize the fruits. The experimental findings demonstrate that Precision, Recall, mAP 0.5, and mAP 0.5:0.95 were 90.4%, 86.4%, 89.4%, and 68.5%, respectively, outperforming the existing approaches and having significant implications for autonomous fruit picking systems in orchards." @default.
- W4312355877 created "2023-01-04" @default.
- W4312355877 creator A5041945227 @default.
- W4312355877 creator A5063888661 @default.
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- W4312355877 date "2022-10-07" @default.
- W4312355877 modified "2023-10-16" @default.
- W4312355877 title "A Fruit Detection Method for Vague Environment High-Density Fruit Orchards" @default.
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- W4312355877 doi "https://doi.org/10.1109/gcat55367.2022.9971938" @default.
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