Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (6): 1873-1878.doi: 10.13229/j.cnki.jdxbgxb20180497

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Agricultural surface multiple feature decision fusion disease judgment algorithm based on machine vision

LIU En-ze(),WU Wen-fu()   

  1. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022,China
  • Received:2018-05-22 Online:2018-11-20 Published:2018-12-11

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Abstract:

For the judgment of the lesions of large-scale planted crops, this paper uses machine vision technology to propose an agricultural disease judgment algorithm based on surface multiple feature decision fusion. First the images of multiple agricultural leaves are collected that different surface feature extraction algorithms are applied to each leaf. Second, the feature weights are set and different features are combined to determine the likelihood of lesions in the leaf. Finally, the method of majority voting decision is used to judge the lesions in the region of each leaf. Compare with the artificial judgment method, this algorithm reduces the workload and can provide effective early warning of crop diseases.

Key words: computer application, machine vision, agricultural disease judgment, surface multiple feature, feature weight, majority voting decision

CLC Number: 

  • TP391

Fig.1

Disease judgment algorithm structure diagram"

Fig.2

Leaf image"

Fig.3

Neural network structure"

Fig.4

Leaf image of various lesions"

Table 1

Experimental results"

算法 叶片
状态		 正常叶片 病变叶片
判断正确 正确率/% 判断正确 正确率/%
本文 早期 4976 99.52 4997 99.94
成熟 4945 98.90 4978 99.56
晚期 4948 98.96 4959 99.18
文献
[2]		 早期 4484 89.68 4877 97.54
成熟 4675 93.50 4681 93.62
晚期 4795 95.90 4772 95.44
文献
[3]		 早期 4697 93.94 4576 91.52
成熟 4396 87.92 4484 89.68
晚期 4499 89.98 4373 87.46

Table 2

This paper single leaf judgment"

叶片
状态		 频域特征 空域特征
CRR/% EER/% CRR/% EER/%
早期 87.56 4.63 99.42 0.75
成熟 99.13 1.08 99.24 0.94
晚期 99.74 0.51 84.76 5.03

Table 3

Disease level"

病变叶占比 病变等级 情况划分
0%~10% 0 正常
10%~20% 1 正常
20%~30% 2 正常
30%~40% 3 轻度病变
40%~50% 4 轻度病变
50%~60% 5 中度病变
60%~70% 6 中度病变
70%~80% 7 中度病变
80%~90% 8 重度病变
90%~100% 9 重度病变

Table 4

Results of early period regional disease testing"

序号 单片数 病变投票 占比/% 病变等级 判断结果
1 5163 21 0.4 0 正常
2 4921 24 0.4 0 正常
3 5215 51 0.9 0 正常
4 5751 102 1.8 0 正常
5 4543 54 1.2 0 正常
6 5642 12 0.2 0 正常
7 6124 13 0.2 0 正常
8 4545 47 1.1 0 正常
9 5437 5345 98.3 9 重度病变
10 5421 5235 96.6 9 重度病变

Table 5

Results of mature period regional disease testing"

序号 单片数 病变投票 占比/% 病变等级 判断结果
1 8457 101 1.2 0 正常
2 7542 71 1.0 0 正常
3 8975 56 0.6 0 正常
4 8876 8450 95.2 9 重度病变
5 8524 512 6 0 正常
6 8945 369 4.1 0 正常
7 9207 1023 11.1 1 正常
8 8752 4235 48.4 4 轻度病变
9 8874 8238 92.8 9 重度病变
10 8785 8452 96.2 9 重度病变

Table 6

Results of late period regional disease testing"

序号 单片数 病变投票 占比/% 病变等级 判断结果
1 6845 745 10.9 1 正常
2 6747 865 12.8 1 正常
3 6572 1204 18.3 1 正常
4 7772 7582 97.6 9 重度病变
5 7578 3354 44.3 4 轻度病变
6 7567 1235 16.3 1 正常
7 7862 4542 57.8 5 中度病变
8 6786 1203 17.7 1 正常
9 8524 8453 99.2 9 重度病变
10 8760 8542 97.5 9 重度病变
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