No abstract

The large-scale and precise intelligent breeding mode for dairy cows is the main direction for the development of the dairy industry. Machine vision has become an important technological means for the intelligent breeding of dairy cows due to its non-invasive, low-cost, and multi-behavior recognition capabilities. This review summarizes the recent application of machine vision technology, machine learning, and deep learning in the main behavior recognition of dairy cows. The authors summarized identity recognition technology based on facial features, muzzle prints, and body features of dairy cows; motion behavior recognition technology such as lying, standing, walking, drinking, eating, rumination, estrus; and the recognition of common diseases such as lameness and mastitis. Based on current research results, machine vision technology will become one of the important technological means for the intelligent breeding of dairy cows. Finally, the author also summarized the advantages of this technology in intelligent dairy farming, as well as the problems and challenges faced in the next development.

The current target detection models have the characteristics of large storage, large demand for computing resources and large number of parameters, which are difficult to be implemented on the platform with low computing performance and small storage capacity. In order to reduce the size of the model and improve the detection speed, this paper proposes a new network architecture of mobilenetv2-yolov5s by combining the lightweight network mobilenetv2 with yolov5s Compared with other target detection algorithms, the improved yolov5s has better detection effect. The mobilenetv2-yolov5s network is tested on MS coco data set, and the mAP value is 55.1. While ensuring the map, the detection speed of the algorithm is 31fps, which is 25fps higher than yolov5s.

In order to solve the problem of backward talent training mode in agriculture-related colleges and universities, this paper proposed a scheme to build a smart teaching platform by using cloud architecture, combining virtualization and twinning technology. The intelligent teaching platform is developed using the 5G converged network architecture and cloud edge system architecture. The intelligent teaching platform has realized such teaching modes as real scene teaching, combination of virtual and real teaching, immersive teaching, multi-teacher collaborative teaching and live interactive teaching. The smart teaching platform has established a new model of digital education, with the functions of teaching, teaching research, teaching management and teaching evaluation, and provides smart teaching cloud services for teachers and students of agriculture-related colleges and universities as well as external tutors. The research of multi-dimensional evaluation system solves the precise management of teaching process. The teaching effect has been significantly improved, and the management cost has been reduced, which meets the goal of training new agricultural talents in agricultural and forestry colleges.