uqtwei2/PlantWild

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, containing 18,542 images of 89 classes.

We invited experts in the field of agriculture to refine our dataset. We also added images of new types of diseases according to experts' suggestions. The obtained dataset, PlantWild_v2, has enhanced data reliability and the number of classes has been expanded to 115.

Both versions can be downloaded.