Application status and prospect of machine learning in unmanned farm

The successful application of machine learning in biological information and face recognition has provided an impetus for the development of unmanned farms. This article first explained the basic concepts of unmanned farms and machine learning and then analyzed the application of machine learning in plantation and animal husbandry. In terms of the planting industry, this paper explained the application in field weed identification, crop disease, and insect pest detection, and crop yield prediction. In the aspect of animal husbandry, it analyzed the application status of machine learning in the accurate identification and classification of fish, pigs, and other livestock, fish feeding decision-making systems, and chicken and cattle production line prediction. It was pointed out that machine learning has the disadvantages of difficulty obtaining training samples and labeling, performance defects of embedded chips, and lack of professional talents. Future researches should focus on establishing a generic unmanned farm database and studying expert systems that can predict animal health and real-time monitoring of animal growth environment conditions. Future researches should also strengthen the embedded research of machine learning to combine machine learning with 5 G, big data, sensors, and other technologies.