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Sensors (Basel). 2020 Jan 21;20(3). pii: E578. doi: 10.3390/s20030578.

A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network.

Li D1,2, Wang R1, Xie C1, Liu L1,2, Zhang J1, Li R1,2, Wang F1,2, Zhou M1,2, Liu W3.

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Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China.
National Agro-Tech Extension and Service Center, Beijing 100125, China.


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 real-time crop diseases and pests video detection system in the future, a deep learning-based 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 still-image detector for detection, and finally synthesized the frames into video. In the still-image detector, we used faster-RCNN as the framework. We used image-training models to detect relatively blurry videos. Additionally, a set of video-based 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, ResNet-50, ResNet-101 backbone system and YOLOv3 with our experimental environment.


deep convolutional neural network; deep learning; rice diseases and pests; video detection; video metrics

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