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

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  • Hua Huang

    (College of Engineering and Technology, Southwest University, Chongqing 400716, China
    These authors contributed equally to this work and should be considered co-first authors.)

  • Yinfeng Liu

    (College of Engineering and Technology, Southwest University, Chongqing 400716, China
    These authors contributed equally to this work and should be considered co-first authors.)

  • 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)

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.

Suggested Citation

  • Hua Huang & Yinfeng Liu & Shiping Zhu & Chuan Feng & Shaoqi Zhang & Lei Shi & Tong Sun & Chao Liu, 2024. "Detection of Mechanical Damage in Corn Seeds Using Hyperspectral Imaging and the ResNeSt_E Deep Learning Network," Agriculture, MDPI, vol. 14(10), pages 1-16, October.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1780-:d:1495422
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    References listed on IDEAS

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    1. Xiaoyu Li & Yuefeng Du & Lin Yao & Jun Wu & Lei Liu, 2021. "Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm," Agriculture, MDPI, vol. 11(12), pages 1-17, December.
    2. Deng, Haiyan & Zheng, Wangyi & Shen, Zhiyang & Štreimikienė, Dalia, 2023. "Does fiscal expenditure promote green agricultural productivity gains: An investigation on corn production," Applied Energy, Elsevier, vol. 334(C).
    3. Olaf Erenstein & Moti Jaleta & Kai Sonder & Khondoker Mottaleb & B.M. Prasanna, 2022. "Global maize production, consumption and trade: trends and R&D implications," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(5), pages 1295-1319, October.
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