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license: cc-by-nc-nd-4.0
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Existing deep-learning methods have achieved remarkable performance in recognizing in-laboratory plant disease 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, PlantWild, which contains the largest number of disease classes but also text-based descriptions for each disease. 
PlantWild is currently the largest dataset containing wild plant disease images.