Rape Plant Disease Recognition Method of Multi-Feature Fusion Based on D-S Evidence Theory
Abstract
:1. Introduction
2. Related Work
2.1. Feature Extraction
2.1.1. Color Feature
2.1.2. Texture Feature
2.2 Dempster-Shafer Evidence Theory
2.2.1. Definition of the Dempster-Shafer Evidence Theory
2.2.2. The Construction of BPA
2.2.3. Decision-Making Rules
- is the largest number in the final BPA;
- The difference between and the belief of any other category should be larger than a threshold (let it be );
- If the above three rules are not met, the recognition result is identified as “uncertain”.
3. Proposed Method
3.1. Decision-Making Improvement
4. Experiments and Analysis
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Zhu, Z.; Yi, H.; Xiaoyu, L.; Dongdong, W.; Chenglong, W.; Hailong, T. Automatic detecting and grading method of potatoes based on machine vision. Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agric. Eng. 2012, 28, 178–183. [Google Scholar]
- Yao, Q.; Guan, Z.; Zhou, Y.; Tang, J.; Hu, Y.; Yang, B. Application of Support Vector Machine for Detecting Rice Diseases Using Shape and Color Texture Features. In Proceedings of the International Conference on Engineering Computation (ICEC 2009), Hong Kong, China, 2–3 May 2009; pp. 79–83.
- Phadikar, S.; Sil, J. Rice disease identification using pattern recognition techniques. In Proceedings of the IEEE International Conference on Computer and Information Technology, Khulna, Bangladesh, 24–27 December 2008; pp. 420–423.
- Amlekar, M.; Manza, R.R.; Yannawar, P.; Gaikwad, A.T. Leaf Features Based Plant Classification Using Artificial Neural Network. IBMRD’s J. Manag. Res. 2014, 3, 224–232. [Google Scholar]
- Komi, P.J.; Jackson, M.R.; Parkin, R.M. Plant Classification Combining Colour and Spectral Cameras for Weed Control Purposes. In Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE 2007), Vigo, Spain, 4–7 June 2007; pp. 2039–2042.
- Tao, H.; Zhao, L.; Xi, J.; Yu, L.; Wang, T. Fruits and vegetablesrecognition based on color and texture features. Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agric. Eng. 2014, 30, 305–311. [Google Scholar]
- Li, X.; Zhu, W.; Kong, L.; Hua, X. Method of Multi-feature Fusion Based on SVM and D-S Evidence Theory in Weed Recognition. Trans. Chin. Soc. Agric. Mach. 2011, 42, 163–168. [Google Scholar]
- Zou, X.; Ding, W.; Chen, C.; Liu, D. Classification of rice planthopper based on improved gray level co-occurrence matrix and particle swarm algorithm. Trans. Chin. Soc. Agric. Eng. 2014, 30, 138–144. [Google Scholar]
- Ojala, T.; Pietikäinen, M.; Mäenpää, T. Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24, 971–987. [Google Scholar] [CrossRef]
- Wang, G.D.; Zhang, P.L.; Ren, G.Q.; Kou, X. Texture Feature Extraction Method Fused with LBP and GLCM. Comput. Eng. 2012, 38, 199–201. [Google Scholar]
- Sun, X.; Wang, J.; Chen, R.; She, M.F.; Kong, L. Multi-scale local pattern co-occurrence matrix for textural image classification. In Proceedings of the International Joint Conference on Neural Networks, Brisbane, Australia, 10–15 June 2012; pp. 1–7.
- Han, D.Q.; Han, C.Z.; Deng, Y.; Yang, Y. Weighted Combination of Conflicting Evidence Based on Evidence Variance. Acta Electron. Sin. 2011, 39, 153–157. [Google Scholar]
Disease Category | Feature | BPA | Recognition Result | ||
---|---|---|---|---|---|
m () | m () | m () | |||
Black spot | Color Matrix | 0.4152 | 0.4805 | 0.1043 | |
GLCM based on ULBP | 0.4614 | 0.3872 | 0.1514 | ||
fusion | 0.4869 | 0.4730 | 0.0401 | uncertain | |
This paper | 0.7803 | 0.6790 | Black spot | ||
Bacterial black spot | Color Matrix | 0.5859 | 0.3387 | 0.0754 | |
GLCM based on ULBP | 0.1893 | 0.2669 | 0.5438 | ||
fusion | 0.4578 | 0.3731 | 0.1691 | uncertain | |
This paper | 0.5472 | 0.8096 | Bacterial black spot | ||
Downy mildew | Color Matrix | 0.6106 | 0.0117 | 0.3777 | |
GLCM based on ULBP | 0.3218 | 0.1669 | 0.5113 | ||
fusion | 0.5018 | 0.0050 | 0.4932 | uncertain | |
This paper | 0.6678 | 0.8272 | Downy mildew |
Type of Disease | Training Sample Number/Testing Sample Number | Correct Identification Number | Recognition Rate (%) |
---|---|---|---|
Black spot | 80/49 | 47 | 95.92 |
Bacterial black spot | 40/21 | 20 | 95.24 |
Downy mildew | 80/33 | 33 | 100.00 |
Total | 200/103 | 100 | 97.09 |
Algorithm | Recognition Rate (%) | Recognition Time (ms) |
---|---|---|
HSI color matrix | 85.44 | 133.20 |
HSI color co-occurrence matrix | 82.52 | 182.73 |
HSI color matrix +HSI color co-occurrence matrix +D-S evidence theory | 91.26 | 203.70 |
color histogram +HSV component co-occurrence matrix | 91.26 | 187.15 |
Lab color matrix +XYZ Chromaticity Moments | 90.29 | 128.97 |
Color matrix +GLCM | 92.23 | 141.86 |
This paper | 97.09 | 232.03 |
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Hu, M.; Bu, X.; Sun, X.; Yu, Z.; Zheng, Y. Rape Plant Disease Recognition Method of Multi-Feature Fusion Based on D-S Evidence Theory. Math. Comput. Appl. 2017, 22, 18. https://doi.org/10.3390/mca22010018
Hu M, Bu X, Sun X, Yu Z, Zheng Y. Rape Plant Disease Recognition Method of Multi-Feature Fusion Based on D-S Evidence Theory. Mathematical and Computational Applications. 2017; 22(1):18. https://doi.org/10.3390/mca22010018
Chicago/Turabian StyleHu, Min, Xiangyu Bu, Xiao Sun, Zixi Yu, and Yaona Zheng. 2017. "Rape Plant Disease Recognition Method of Multi-Feature Fusion Based on D-S Evidence Theory" Mathematical and Computational Applications 22, no. 1: 18. https://doi.org/10.3390/mca22010018