关键词: 苹果叶部病害识别, 苹果病害叶片图像, 最近邻分类器, 二维子空间学习维数约简

Abstract: The accuracy and real-time recognition of apple diseases is an important research area. By the symptoms of the apple leaves, correct and fast identification of apple leaf diseases is the basis of preventing and controlling the apple leaf diseases. Because the apple leaves are different from each other in shape, color and texture, many classical pattern recognition methods are not effectively applied to recognizing the apple diseases. In this paper, a novel two-Dimensionality Subspace Learning Dimensionality Reduction method（2DSLDR） is proposed for the apple leaf disease recognition. By 2DSLDR, the multi-class sample points in high-dimensional space are projected to low-dimensional subspace and the optimal low-dimensional features from the point of view of classification are extracted. After being projected into a low-dimensional subspace, the sample points in the same class are close to each other, but the different-class sample points are far away from each other. 2DSLDR directly deals with the leaf images and does not calculate the inverse matrix, so the hard-problem in the classical plant recognition methods to extract and select the feature is overcome, and the small sample size problem occurring in traditional subspace discriminant analysis methods is naturedly avoided, and much computational time is saved by using 2DSLDR for dimensionality reduction. The recognition experiments are performed on a real apple disease leaf image database and compared with the traditional disease recognition methods and the supervised subspace learning algorithms in recognition performance. The experiment results show that the proposed method is effective and feasible for apple leaf disease recognition. The mean correct recognition ratio is above 90%.

Key words: apple leaf disease recognition, apple disease leaf image, nearest neighborhood classifier, Two-dimensionality Subspace Learning Dimensionality Reduction（2DSLDR）