Detection of Mechanical Damage in Corn Seeds Using Hyperspectral Imaging and the ResNeSt_E Deep Learning Network

Hua Huang, Yinfeng Liu, Shiping Zhu (), Chuan Feng, Shaoqi Zhang, Lei Shi, Tong Sun and Chao Liu
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Hua Huang: College of Engineering and Technology, Southwest University, Chongqing 400716, China
Yinfeng Liu: College of Engineering and Technology, Southwest University, Chongqing 400716, China
Shiping Zhu: College of Engineering and Technology, Southwest University, Chongqing 400716, China
Chuan Feng: College of Engineering and Technology, Southwest University, Chongqing 400716, China
Shaoqi Zhang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Lei Shi: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Tong Sun: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Chao Liu: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China

Agriculture, 2024, vol. 14, issue 10, 1-16

Abstract: Corn is one of the global staple grains and the largest grain crop in China. During harvesting, grain separation, and corn production, corn is susceptible to mechanical damage including surface cracks, internal cracks, and breakage. However, the internal cracks are difficult to observe. In this study, hyperspectral imaging was used to detect mechanical damage in corn seeds. The corn seeds were divided into four categories that included intact, broken, internally cracked, and surface-crackedtv. This study compared three feature extraction methods, including principal component analysis (PCA), kernel PCA (KPCA), and factor analysis (FA), as well as a joint feature extraction method consisting of a combination of these methods. The dimensionality reduction results of the three methods (FA + KPCA, KPCA + FA, and PCA + FA) were combined to form a new combined dataset and improve the classification. We then compared the effects of six classification models (ResNet, ShuffleNet-V2, MobileNet-V3, ResNeSt, EfficientNet-V2, and MobileNet-V4) and proposed a ResNeSt_E network based on the ResNeSt and efficient multi-scale attention modules. The accuracy of ResNeSt_E reached 99.0%, and this was 0.4% higher than that of EfficientNet-V2 and 0.7% higher than that of ResNeSt. Additionally, the number of parameters and memory requirements were reduced and the frames per second were improved. We compared two dimensionality reduction methods: KPCA + FA and PCA + FA. The classification accuracies of the two methods were the same; however, PCA + FA was much more efficient than KPCA + FA and was more suitable for practical detection. The ResNeSt_E network could detect both internal and surface cracks in corn seeds, making it suitable for mobile terminal applications. The results demonstrated that detecting mechanical damage in corn seeds using hyperspectral images was possible. This study provides a reference for mechanical damage detection methods for corn.

Keywords: corn seed; hyperspectral image; mechanical damage; combined dataset; ResNeSt_E (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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