Abstract
Objective: To accurately distinguish intact peanut, nut damaged peanut and epidermis damaged peanut. Methods: A peanut seed integrity detection scheme based on deep learning convolution neural network (CNN) was proposed. The peanut seed color selection system was established and a peanut seed image database was also established; The improved density peak clustering (DPC) algorithm was used to adaptively compress the CNN convolution kernel to effectively balance the network depth and operation efficiency; The improved sparrow search algorithm was used to optimize the CNN super parameter configuration and network structure, and the CNN model suitable for peanut grain integrity detection was obtained. Results: Compared with other detection methods, this scheme improved the recognition accuracy by about 5.41%~13.92%, and the detection time of single image of peanut grain was shortened by about 16.9%. Conclusion: This method effectively improves the accuracy and real-time of peanut grain integrity detection.
Publication Date
6-30-2022
First Page
24
Last Page
29,36
DOI
10.13652/j.spjx.1003.5788.2022.90149
Recommended Citation
Jun-feng, ZHANG and Zhan-lei, SHANG
(2022)
"Peanut kernel integrity detection based on deep learning convolution neural network,"
Food and Machinery: Vol. 38:
Iss.
5, Article 5.
DOI: 10.13652/j.spjx.1003.5788.2022.90149
Available at:
https://www.ifoodmm.cn/journal/vol38/iss5/5
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