A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a realtime crop diseases and pests video detection system in the future, a deep learningbased video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the stillimage detector for detection, and finally synthesized the frames into video. In the stillimage detector, we used fasterRCNN as the framework. We used imagetraining models to detect relatively blurry videos. Additionally, a set of videobased evaluation metrics based on a machine learning classifier was proposed, which reflected the quality of video detection effectively in the experiments. Experiments showed that our system with the custom backbone was more suitable for detection of the untrained rice videos than VGG16, ResNet50, ResNet101 backbone system and YOLOv3 with our experimental environment.

Deep localization model for intrarow crop detection in paddy field Abstract   Automated and precise rice plant localization is crucial for the mechanization of rice production, which can facilitate targeted spraying, sitespecific fertilization, and mechanized weeding etc. Existing approaches adopted thus far have mainly focused on interrow weed detection or rice seedling row detection. Nevertheless, techniques for intrarow individual rice plant positioning continue to face major challenges induced by the specific paddy field environments or complex morphology of rice plant. This paper proposed a new deep localization network for intrarow rice detection at the single plant level in a paddy field. This method designed a twostage model. The module in stage 1 identified potential locations containing rice plants in the entire image. The module in stage 2 predicted the confidence of rice plant identification and refined the corresponding box bounds. The twostage processing modules shared a deep backbone network for learning fullimage convolutional features and are combined into a unified framework to facilitate an endtoend training. In addition, we constructed a rice plant detection dataset and proposed a taskoriented evaluation method for performance verification of the algorithm. Experiment results showed the proposed deep model achieved a high localization accuracy of 93.22% and a high testing speed of 15 fps, verifying the effectiveness and efficiency of the method. Using this method, we can develop techniques for finerlevel agriculture production, such as spraying and weed control, to achieve healthy and economical rice yields.