Deep Learning for Pollen Sac Detection and Measurement on Honeybee Monitoring Video This paper introduces a new model which applies deep learning techniques to pollen sac detection and measurement on honeybee monitoring video. The outcome of this model is a measurement of the number of pollen sacs being brought to the beehive, so that beekeepers will not need to open beehives frequently to check food storage. The pollen sacs are detected on individual bee images which are collected using a bee detection model on the entire video frame. The pollen detection model is built using a deep convolutional neural network. The architecture is Faster RCNN with VGG16 core network. The network is trained to detect pollen sacs so the individual bee images are identified as either pollen or nonpollen bee images. This pollen sac detection model is then combined with a bee tracking model, so that each flying bee tracked on successive video frames is identified as carrying pollen or not. Finally, the number of pollencarrying bees can be counted. The experimental results show that the measurement error of this model is 7%. The deep learning model improves the results from the conventional image processing method, which produced 33% measurement error.

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.