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Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

Author

Listed:
  • Xiaoyu Li

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Yuefeng Du

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Lin Yao

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Jun Wu

    (Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China)

  • Lei Liu

    (College of Engineering, China Agricultural University, Beijing 100083, China)

Abstract

At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems with existing detection methods for judging the integrity of corn kernels, such as low accuracy, poor reliability, and difficulty in adapting to the complicated and changeable harvesting environment, this paper investigates a broken corn kernel detection device for combine harvesters by using the yolov4-tiny model. Hardware construction is first designed to acquire continuous images and processing of corn kernels without overlap. Based on the images collected, the yolov4-tiny model is then utilized for training recognition of the intact and broken corn kernels samples. Next, a broken corn kernel detection algorithm is developed. Finally, the experiments are carried out to verify the effectiveness of the broken corn kernel detection device. The laboratory results show that the accuracy of the yolov4-tiny model is 93.5% for intact kernels and 93.0% for broken kernels, and the value of precision, recall, and F1 score are 92.8%, 93.5%, and 93.11%, respectively. The field experiment results show that the broken kernel rate obtained by the designed detection device are in good agreement with that obtained by the manually calculated statistic, with differentials at only 0.8%. This study provides a technical reference of a real-time method for detecting a broken corn kernel rate.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:12:p:1238-:d:697700
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    References listed on IDEAS

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    1. Oostwal, Elisa & Straat, Michiel & Biehl, Michael, 2021. "Hidden unit specialization in layered neural networks: ReLU vs. sigmoidal activation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
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    Cited by:

    1. Haotian Pei & Youqiang Sun & He Huang & Wei Zhang & Jiajia Sheng & Zhiying Zhang, 2022. "Weed Detection in Maize Fields by UAV Images Based on Crop Row Preprocessing and Improved YOLOv4," Agriculture, MDPI, vol. 12(7), pages 1-18, July.
    2. Yichen Qiao & Yaohua Hu & Zhouzhou Zheng & Huanbo Yang & Kaili Zhang & Juncai Hou & Jiapan Guo, 2022. "A Counting Method of Red Jujube Based on Improved YOLOv5s," Agriculture, MDPI, vol. 12(12), pages 1-20, December.
    3. Yanxin Hu & Gang Liu & Zhiyu Chen & Jiaqi Liu & Jianwei Guo, 2023. "Lightweight One-Stage Maize Leaf Disease Detection Model with Knowledge Distillation," Agriculture, MDPI, vol. 13(9), pages 1-22, August.

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