A Method for Plant Diseases Detection Based on Transfer Learning and Data Enhancement

With the wide application of deep learning in the field of agriculture, how to quickly and accurately identify and process structured and unstructured monitoring data to support intelligent decision-making has become an important research direction in the field of intelligent agriculture. In this paper, the multiple diseases of wild and introduced flowers such as canker, gray mold, brown spot and leaf blight were taken as the research objects, and the disease samples were collected according to the growth cycle of flowers, so as to construct a data set of flower diseases and insect pests under natural scenes. Based on Faster-RCNN framework, a three-stage pest and disease detection model PD-IFRCNN is proposed, which integrates transfer learning and data enhancement technology. In order to ensure the effectiveness of the model, this paper verifies the influence of the disequilibrium of the disease category of the self-built data set on the cost of wrong classification from two different perspectives: category balancing and label balancing, and makes a comparative analysis with the SSD model that takes VGG16 as the feature to extract the network. Experiments show that the proposed method can adapt to the detection of diseases with different scales and multiple leaves in natural scenes, and has high recognition accuracy. The proposed method not only breaks the traditional manual detection method of plant diseases, but also provides a new research idea for the recognition of plant diseases. It fundamentally solves the problems such as the lack of pertinence of traditional disease control and the lack of timely control caused by the inconveniences of manual detection due to bad weather and poor operating environment.