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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Bibliographic Details
Published in:Journal of food engineering Vol. 379; p. 112133
Main Authors: Wang, Jingwei, Bai, Xiaopeng, Xu, Daochun, Li, Wenbin, Tong, Siyuan, Zhang, Jiaming
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2024
Subjects:
algorithms
Deep learning
mechanization
prototypes
Semantic segmentation
Sorting
Walnut
walnut hulls
walnuts
Deep learning
Walnut
Semantic segmentation
Sorting
ISSN:0260-8774
Online Access:Get full text
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Summary: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