  Existing plant disease classification models have achieved remarkable
performance in recognizing in-laboratory diseased images. However, their
performance often significantly degrades in classifying in-the-wild images.
Furthermore, we observed that in-the-wild plant images may exhibit similar
appearances across various diseases (i.e., small inter-class discrepancy) while
the same diseases may look quite different (i.e., large intra-class variance).
Motivated by this observation, we propose an in-the-wild multimodal plant
disease recognition dataset that contains the largest number of disease classes
but also text-based descriptions for each disease. Particularly, the newly
provided text descriptions are introduced to provide rich information in
textual modality and facilitate in-the-wild disease classification with small
inter-class discrepancy and large intra-class variance issues. Therefore, our
proposed dataset can be regarded as an ideal testbed for evaluating disease
recognition methods in the real world. In addition, we further present a strong
yet versatile baseline that models text descriptions and visual data through
multiple prototypes for a given class. By fusing the contributions of
multimodal prototypes in classification, our baseline can effectively address
the small inter-class discrepancy and large intra-class variance issues.
Remarkably, our baseline model can not only classify diseases but also
recognize diseases in few-shot or training-free scenarios. Extensive
benchmarking results demonstrate that our proposed in-the-wild multimodal
dataset sets many new challenges to the plant disease recognition task and
there is a large space to improve for future works.
