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.

中文翻译：

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苹果叶部病害识别的跨数据集判别子空间学习算法

苹果在我国种植面积大、产量高，但易受病害影响。人工智能技术在苹果叶部病害识别方面取得了良好的效果。然而，复杂环境下的训练和测试数据往往来自不同的采集场景。训练和测试数据集存在显着差异，导致识别性能显着下降。为了提高苹果叶病识别的泛化能力，我们利用迁移学习、低秩稀疏表示和最大边缘邻域保持嵌入（NPE）的思想，开发了一种跨数据集判别子空间学习（CDDSL）算法。数据集场景。首先，基于迁移学习和低秩稀疏表示，每个目标样本都可以由源域样本的线性组合来表示。对噪声矩阵的稀疏约束削弱了样本数据中噪声的影响。然后，CDDSL逐步将子空间特征投影到语义空间中并建立判别分类器。同时，CDDSL利用最大边缘NPE来提高投影特征的类内紧致性和类间可分离性，从而增强图像识别性能。在真实苹果叶数据集上进行的实验验证了CDDSL算法在跨数据集场景下的有效性。