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

Hongwei Cui, Qiang Zhang, Jinsong Zhang, Zidan Wu and Wenfu Wu
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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
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

Agriculture, 2021, vol. 11, issue 5, 1-15

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.

Keywords: grain storage; temperature contour map; BP neural network; grain inventory (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: 2021
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