Agricultural Disease Image Dataset for Disease Identification Based on Machine Learning

Identification and control of agricultural diseases and pests is significant for improving agricultural yield. Food and Agriculture Organization of the United Nations reported that more than one-third of the annual natural loss is caused by agricultural diseases and pests. Traditional artificial identification is not accurate enough since it relies on subjective experience. In recent years, computer vision and machine learning, which require large-scale training samples, have been widely used for crop disease image identification. Therefore, building large training dataset and studying new classifier modeling methods are very important. Accordingly, on the one hand, we have constructed an agricultural disease image dataset which covers many research fields such as image acquisition, segmentation, classification, marking, storage and modeling. The dataset currently has about 15,000 high-quality agricultural disease images, including field crops such as rice and wheat, fruits and vegetables such as cucumber and grape, etc. And it will continue to grow. On the other hand, with the support of this dataset, we investigated a disease image identification method based on different kinds of transfer learning with deep convolutional neural network and achieved good results. The paper has two contributions. First, the constructed agricultural disease image dataset provides valuable data resources for the research of agricultural disease image identification. Secondly, the proposed disease identification method based on transfer learning can provide reference for disease diagnosis where the available labeled samples are still limited.