Online sorting of surface defective walnuts based on deep learning

To address concerns regarding walnut shell damage and inadequate sorting precision during the mechanized sorting of walnuts, a walnut automatic sorting machine was designed based on deep learning and experimental research. Initially, the rationality of the design was verified through experiment. The...

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Vydané v:Journal of food engineering Ročník 379; s. 112133
Hlavní autori: Wang, Jingwei, Bai, Xiaopeng, Xu, Daochun, Li, Wenbin, Tong, Siyuan, Zhang, Jiaming
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.10.2024
Predmet:
algorithms
Deep learning
mechanization
prototypes
Semantic segmentation
Sorting
Walnut
walnut hulls
walnuts
Deep learning
Walnut
Semantic segmentation
Sorting
ISSN:0260-8774
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Shrnutí:To address concerns regarding walnut shell damage and inadequate sorting precision during the mechanized sorting of walnuts, a walnut automatic sorting machine was designed based on deep learning and experimental research. Initially, the rationality of the design was verified through experiment. Then, three deep learning semantic segmentation algorithms, namely PSPnet, U-net, and Deeplabv3+, were selected to train walnut detection models. Results indicated that the U-net algorithm proved to be the most effective, achieving a Mean Intersection over Union of 96.71% and a Mean Pixel Accuracy value of 98.52%. Finally, performance tests were conducted on the prototype machine, yielding results with an average sorting efficiency of 51.70 kg/h, an average loss rate of 6.50%, and an average accuracy of sorting walnuts of 92.98%. The findings can provide insights for future structural improvements and operational parameter optimization of walnut automatic sorting machines. •Design of the walnut automatic sorting machine and experiment of key component.•Excellent prediction ability of walnut detection model by U-net algorithm.•Good sorting efficiency, walnut loss rate and sorting walnuts accuracy of the prototype.
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ISSN:0260-8774
DOI:10.1016/j.jfoodeng.2024.112133