A method for counting and classifying aphids using computer vision Abstract   Aphids are insects that attack crops and cause damage directly, by consuming the sap of plants, and indirectly, by vectoring microorganisms that can cause diseases. Cereal crops are hosts for many aphid species, including Rhopalosiphum padi (an economically important aphid species). Recording and classifying aphids are necessary for evaluating and predicting crop damage. Thus, serving as a basis for decision making on the utilization of control measures. It can also be useful to evaluate plant resistance to aphids. Traditionally, the recording process is manual and depends on magnification and welltrained staff. The manual counting is also a timeconsuming process and susceptible to errors. With this in mind, this paper presents a method and software to automate the counting and classification of Rhopalosiphum padi using image processing, computer vision, and machine learning methods. The text also presents a comparison of manually counts from experts and values obtained with the software, considering 40 samples. The results showed strong positive correlation in counting and classification (      r    s      xe2x80xafxe2x80xaf0.92579) and measurement (rxe2x80xafxe2x80xaf0.9799). Concluding, the software proved to be reliable and useful to aphid population monitoring studies.

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.