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Classification of Grain Storage Inventory Modes Based on Temperature Contour Map of Grain Bulk Using Back Propagation Neural Network

Author

Listed:
  • Hongwei Cui

    (College of Biology and Agricultural Engineering, Jilin University, Changchun 130022, China)

  • Qiang Zhang

    (College of Biology and Agricultural Engineering, Jilin University, Changchun 130022, China
    Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada)

  • Jinsong Zhang

    (College of Biology and Agricultural Engineering, Jilin University, Changchun 130022, China)

  • Zidan Wu

    (College of Biology and Agricultural Engineering, Jilin University, Changchun 130022, China)

  • Wenfu Wu

    (College of Biology and Agricultural Engineering, Jilin University, Changchun 130022, China
    Jilin Business and Technology College, Changchun 130507, China)

Abstract

Inventory modes classification can reduce the workload of grain depot management and it is time-saving, not labor-intensive. This paper proposed a method of using a temperature contour map converted from digital temperature data to classify stored grain inventory modes in a large bulk grain warehouse, which mainly included detection of inventory changes and routine operations performed (aeration). The back propagation (BP) neural network was used in this method to identify and classify grain storage inventory modes based on the temperature contour map for helping grain depot management work. The method extracted and combined color coherence vector (CCV), texture feature vector (TFV) and smoothness feature vector (SFV) of temperature contour maps as the input vector of the BP neural network, and used inventory modes as the output vector. The experimental results indicated that the accuracy of the BP neural network with vector (CCV and TFV and SFV) as the input vector was about 93.9%, and its training time and prediction time were 320 and 0.12 s, respectively.

Suggested Citation

  • Hongwei Cui & Qiang Zhang & Jinsong Zhang & Zidan Wu & Wenfu Wu, 2021. "Classification of Grain Storage Inventory Modes Based on Temperature Contour Map of Grain Bulk Using Back Propagation Neural Network," Agriculture, MDPI, vol. 11(5), pages 1-15, May.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:5:p:451-:d:555607
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    References listed on IDEAS

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    1. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
    2. Ashish Manandhar & Paschal Milindi & Ajay Shah, 2018. "An Overview of the Post-Harvest Grain Storage Practices of Smallholder Farmers in Developing Countries," Agriculture, MDPI, vol. 8(4), pages 1-21, April.
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