Fewshot cotton pest recognition and terminal realization Abstract   Xinjiang is the major cottonproducing area in China, also well known in the world for its highquality cotton. The growth and quality of cotton are closely related to the pest attack, but it is difficult for farmers to manually recognize all the types of pests because of the similar appearance. To solve this problem, we propose a fewshot cotton pest recognition method, which only needs few raw training data, quite different from the typical deep learning methods. We use two datasets to verify the effectiveness and feasibility of the fewshot model, one is the National Bureau of Agricultural Insect Resources (NBAIR), the other is a dataset with the natural scenes. The convolutional neural network (CNN) is adopted to extract feature vectors of images. The CNN feature extractor is trained by the triplet loss to distinguish different pest species to ensure the system robustness. Furthermore, the fewshot recognition model is finally running in an embedded terminal, based on the compiled convolutional and maxpooling circuit in the FPGA and the control program in the ARM. The running speed reaches 2 frames per second and can be faster by the further parallelism in hardware. The testing accuracy for the two datasets is 95.4% and 96.2% respectively, shown the generalization ability of the proposed fewshot model. Moreover, this work can also be regarded as a positive attempt to combine the software and hardware for the landing of intelligent algorithms in the agricultural applications.

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.