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Math. Comput. Appl. 2017, 22(1), 18;

Rape Plant Disease Recognition Method of Multi-Feature Fusion Based on D-S Evidence Theory

1,2,* , 1,2
Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory, Hefei 230009, Anhui
School of Computer and Information, Hefei University of Technology, Anhui 230009, China
Author to whom correspondence should be addressed.
Academic Editor: Junjie Cao
Received: 19 October 2016 / Revised: 14 December 2016 / Accepted: 20 January 2017 / Published: 15 February 2017
(This article belongs to the Special Issue Information and Computational Science)
Full-Text   |   PDF [238 KB, uploaded 15 February 2017]


In view of the low accuracy and uncertainty of the traditional rape plant disease recognition relying on a single feature, this paper puts forward a rape plant disease recognition method based on Dempster-Shafer (D-S) evidence theory and multi-feature fusion. Firstly, color matrix and gray-level co-occurrence matrix are extracted as two kinds of features from rape plant images after processing. Then by calculating the Euclidean distance between the test samples and training samples, the basic probability assignment function can be constructed. Finally, the D-S combination rule of evidence is used to achieve fusion, and final recognition results are given by using the variance. This method is used to collect rape plant images for disease recognition, and recognition rate arrives at 97.09%. Compared with other methods, experimental results show that the method is more effective and with lower computational complexity. View Full-Text
Keywords: rape plant diseases; multi-feature; Dempster-Shafer evidence theory; variance rape plant diseases; multi-feature; Dempster-Shafer evidence theory; variance
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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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.

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