Cross-dataset discriminant subspace learning algorithm for apple leaf diseases identification

Abstract

Apple has a large planting area and high yield in China, but it is easily affected by diseases. Artificial intelligence technology has achieved good results in apple leaf disease identification. However, the training and testing data in complex environments often come from different collection scenarios. There are significant differences in the training and testing datasets, which leads to a significant decline in the identification performance. To improve the generalization ability of apple leaf disease identification, we develop a cross-dataset discriminant subspace learning (CDDSL) algorithm by utilizing the idea of transfer learning, low-rank sparse representation, and maximum margin neighborhood preserving embedding (NPE) to cross-dataset scenarios. Firstly, based on the transfer learning and low-rank sparse representation, each target sample can be represented by a linear combination of source domain samples. The sparse constraint on the noise matrix weakens the influence of noise in the sample data. Then, CDDSL progressively projects the subspace features into the semantic space and establishes a discriminant classifier. Meanwhile, CDDSL utilizes the maximal margin NPE to improve the intraclass compactness and interclass separability of projection features, which can enhance the image identification performance. The experiments conducted on real-world apple leaf datasets verify the effectiveness of the CDDSL algorithm in cross-dataset scenario.