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Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Author

Listed:
  • Peng Xu

    (College of Information and Communication Engineering, Hainan University, Haikou 570228, China)

  • Qian Tan

    (College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China)

  • Yunpeng Zhang

    (College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China)

  • Xiantao Zha

    (College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China)

  • Songmei Yang

    (College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China)

  • Ranbing Yang

    (College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China)

Abstract

Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry.

Suggested Citation

  • Peng Xu & Qian Tan & Yunpeng Zhang & Xiantao Zha & Songmei Yang & Ranbing Yang, 2022. "Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning," Agriculture, MDPI, vol. 12(2), pages 1-16, February.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:232-:d:743132
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    Citations

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    Cited by:

    1. Weidong Zhu & Jun Sun & Simin Wang & Jifeng Shen & Kaifeng Yang & Xin Zhou, 2022. "Identifying Field Crop Diseases Using Transformer-Embedded Convolutional Neural Network," Agriculture, MDPI, vol. 12(8), pages 1-19, July.
    2. Lili Yang & Changlong Wang & Jianfeng Yu & Nan Xu & Dongwei Wang, 2023. "Method of Peanut Pod Quality Detection Based on Improved ResNet," Agriculture, MDPI, vol. 13(7), pages 1-20, July.
    3. Kadir Sabanci, 2023. "Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars," Sustainability, MDPI, vol. 15(5), pages 1-14, March.
    4. Xianguo Ren & Haiqing Tian & Kai Zhao & Dapeng Li & Ziqing Xiao & Yang Yu & Fei Liu, 2022. "Research on pH Value Detection Method during Maize Silage Secondary Fermentation Based on Computer Vision," Agriculture, MDPI, vol. 12(10), pages 1-17, October.
    5. Dimitre D. Dimitrov, 2023. "Internet and Computers for Agriculture," Agriculture, MDPI, vol. 13(1), pages 1-7, January.
    6. Guangyu Hou & Haihua Chen & Mingkun Jiang & Runxin Niu, 2023. "An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots," Agriculture, MDPI, vol. 13(9), pages 1-31, September.
    7. Yu Wang & Hongyi Bai & Laijun Sun & Yan Tang & Yonglong Huo & Rui Min, 2022. "The Rapid and Accurate Detection of Kidney Bean Seeds Based on a Compressed Yolov3 Model," Agriculture, MDPI, vol. 12(8), pages 1-21, August.

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