Crops ›› 2021, Vol. 37 ›› Issue (1): 200-206.doi: 10.16035/j.issn.1001-7283.2021.01.028

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Study on the Classification of Wheat Grain Quality Based on GWO Optimized SVM

An Juanhua(), Dong Xin, Wang Kejian(), He Zhenxue   

  1. College of Information Science and Technology, Hebei Agricultural University, Baoding 071000, Hebei, China
  • Received:2020-03-18 Revised:2020-05-08 Online:2021-02-15 Published:2021-02-23
  • Contact: Wang Kejian E-mail:2193166210@qq.com;33688415@qq.com

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Abstract:

Wheat grain quality is not only an important determinant of yield and quality but also a comprehensive indicator of breeding adaptability. In order to improve the accuracy of wheat grain grading, at the same time to solve the problem of local optimal solution and slow convergence rate existed in artificial neural networks, we presented a method based on support vector machine (SVM) with gray wolf algorithm optimization (GWO) for grading of wheat grain. First of all to wheat 8805 as the research object, using the image processing technology of wheat grain image preprocessing and extract the grain morphology, color, texture, a total of 21 characteristics. Then, GWO was used to optimize the two parameters (c, σ) of the SVM, and the GWO-SVM model was established to grading of wheat grains. Comparing the GWO optimized SVM algorithm with other algorithms, the results showed that the accuracy of the GWO optimized SVM algorithm for wheat grain classification can reach 95.08%, which is significantly higher than other algorithms.

Key words: Wheat grain, Classification of advantages and disadvantages, Grey wolf optimizer, Support vector machine

Fig.1

Wheat grain RGB original image"

Fig.2

Effect diagram of wheat grain pretreatment"

Table 1

Comparison of GWO-SVM and manual classification results"

等级
Grade		 人工分级结果(粒)
Manual grading results (grain)		 GWO-SVM
正确分级数(粒)
Number of correct grades (grain)		 错误分级数(粒)
Number of error ratings (grain)		 正检率
Correct detection rate (%)		 错检率
False detection rate (%)
优Superior 29 28 1 96.55 3.45
中Medium 20 19 1 95.00 5.00
劣Inferior 12 11 1 91.67 8.33
总计Total 61 58 3 95.08 4.92
均值Mean - - - 94.41 5.59

Table 2

Classification results of different algorithms"

算法
Algorithm		 等级
Grade		 正确分级数(粒)
Number of correct grades (grain)		 错误分级数(粒)
Number of error ratings (grain)		 正检率
Correct detection rate (%)		 错检率
False detection rate (%)
PSO-SVM 优Superior 28 1 96.55 3.45
中Medium 17 3 85.00 15.00
劣Inferior 10 2 83.33 16.67
总计Total 55 6 90.16 9.84
均值Mean - - 88.29 11.71
SVM 优Superior 26 3 89.66 10.34
中Medium 15 5 75.00 25.00
劣Inferior 10 2 83.33 16.67
总计Total 51 10 83.61 16.39
均值Mean - - 82.66 17.34

Table 3

Time results of different algorithms s"

K值K-value SVM GWO-SVM PSO-SVM
3 1.68 4.27 12.27
5 2.72 4.86 14.86
7 3.20 5.01 15.01
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