Abstract: According to the report of Food and Agriculture Organization of the United Nations, the annual natural loss rate caused by agricultural pests and diseases reached more than 37%. Identification and control of agricultural pests and diseases is significant for improving agricultural yield. Traditional manual recognition methods are not accurate enough since they rely on subjective experience. In recent years, computer vision-based methods have developed gradually. These methods are more objective and support real-time online diagnosis. As these methods depend on large-scale training samples, building an image dataset for machine learning modeling is very important for efficiently identifying agricultural diseases and pests. Therefore, we have constructed an image dataset for agricultural diseases and pests research (IDADP) which covers such aspects of agricultural diseases and pests as image acquisition, classification, labeling, storage and modeling. Meanwhile, this image dataset provides online diagnosis of agricultural diseases and related technical consultation services for scholars and agricultural technicians. The image dataset currently has about 200 GB of high-quality agricultural disease images, including field crops such as rice, wheat and corn. Essentially different from existing agricultural disease map resources which mostly contain only 3 to 5 typical symptom images, our dataset consists of the original image data of the same kind of crop diseases with high resolution and high similarity. Each disease has hundreds or even thousands images, which can be used as training samples for machine learning modeling of disease identification. As a standard dataset for machine learning modeling in large data environment, this image dataset will provide valuable basic data resources. And it has important applicability in promoting the development of agricultural disease identification.
Keywords: agricultural disease; disease identification; standard image dataset; machine Learning; training sample