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Learning to recognize novel visual classes from few samples is challenging but promising. Previous studies have shown that few-shot model tends to overfit and lead to poor generalization performance, which is because it finds a biased distribution based on a few samples. In addition, in agriculture-specific domains, there are more serious research challenges such as imbalanced disease distribution, one-shot representation biases, fine-grained recognition, and granularity shift. As far as we know, this study is the first work on the fine-grained "Coarse-to-Fine" few-shot plant disease classification, which classifies "fine-grained novel classes" (specific to disease severity) based on "coarse-grained base classes" (specific to plant species). A complete two-stage in-use calibration strategy is presented in this paper. Firstly, we propose an attention-based inverse Mahalanobis distance weighted prototype calibration module (AIPCM). By transferring statistics from sample-rich coarse-grained base classes to sample-scarce fine-grained novel classes, we achieve prototype calibration for 1-shot sample and obtain an unbiased distribution in the feature space. Secondly, to generate more reasonable decision boundaries, we propose a prior-driven task-adapted decision boundary calibration module (TDBCM) based on class-covariance metric. The original Euclidean/Cosine distance is updated to the Mahalanobis distance by introducing the prior mean and covariance of the high-dimensional features. Experimental results on several datasets demonstrate that our model outperforms the state-of-the-art (SOTA) models. It can be said that our work is a valuable supplement to the domain-specific agricultural applications. [ABSTRACT FROM AUTHOR]

In real-world industrial applications, learning to recognize novel visual categories from a few samples is challenging and promising. Although some efforts have been made in the academic field for few-shot classification studies, there is still a lack of high-precision fine-grained few-shot classification models in some specific fields, especially in the fine-grained agricultural field. As far as we know, this study is the first work on meta-learning few-shot classification for fine-grained plant disease classification (specific to disease severity). We propose a multi-perspective hybrid attention meta-learning model based on a Batch Nuclear-norm constraint. The model explores discriminative features by focusing on key regions, and the hybrid attention module is divided into two sub-modules, soft attention model and patch-hard attention model. The discriminability and diversity constraint module is introduced in the loss function to constrain the Batch Nuclear-norm of the classification matrix, which improves the discriminative properties of the classification model and increases its diversity at the same time. In this paper, a large number of experiments have been carried out on multiple datasets. The experimental results demonstrate that our work has better performance than state-of-the-art models. It can be said that our work is a valuable supplement to the domain-specific industrial application models. [ABSTRACT FROM AUTHOR]