Yuchen Wei, Lisheng Wei*, Tao Ji and Huosheng Hu Pages 331 - 339 ( 9 )
Background: The spot, streak and rust are the most common diseases in maize, all of which require effective methods to recognize, diagnose and handle. This paper presents a novel image classification approach to the high accuracy recognition of these maize diseases.
Methods: Firstly, the k-means clustering algorithm is deployed in LAB color space to reduce the influence of image noise and irrelevant background, so that the area of maize diseases could be effectively extracted. Then the statistic pattern recognition method and gray level co-occurrence matrix (GLCM) method are jointly used to segment the maize disease leaf images for accurately obtaining their texture, shape and color features. Finally, Support Vector Machine (SVM) classification method is used to identify three diseases.
Results: Numerical results clearly demonstrate the feasibility and effectiveness of the proposed method.
Conclusion: Our future work will focus on the investigation of how to use the new classification methods in dimensional and large scale data to improve the recognizing performance and how to use other supervised feature selection methods to improve the accuracy further.
Maize diseases, image processing, color segmentation, gray level co-occurrence matrix, feature extraction, support vector machine.
School of Agricultural Resources and Environment, Heilongjiang University, Harbin, 150000, School of Electrical and Engineering, Anhui Polytechnic University, Wuhu, 241000, School of Electrical and Engineering, Anhui Polytechnic University, Wuhu, 241000, School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ