At present, the quality of material life and richness of the Chinese residents have been improved. Some people have high requirements for quality of their personal lives and put forward the problem of health preservation. Honey, as a nutritious food, is deeply loved by people. There are a large number of trace elements in honey, such as VC, VA, VD, VB1, and VB2. The honey from the Chinese honeybee has a very high nutritional value and plays an important role in the pollination and reproduction of some plants. Therefore, the Chinese honeybee plays a very significant role in the ecological environment. Moreover, it is protected as the main species resource of the country, which also fully proves the importance of the Chinese honeybee. Chinese bees can survive in various ecological and geographical environments in China and have strong heat as well as cold resistance. They can survive in the hot environment in the south and withstand the dynamics of severe cold in the north. At the same time, they can make full use of a small number of honey sources and have strong resistance to a variety of diseases and pests. In fact, there will be a variety of insect invasion problems in the beehive culture of Chinese bees, and it is necessary to accurately detect various diseases and pests during the breeding of Chinese bees. However, there are a large amount of insect invasion and various disease sources in the breeding stage of the Chinese bees. Therefore, in this paper, we use a deep learning algorithm to detect the insect invasion of the Chinese beehive culture and analyze the bee colonies in six bee farms in the province of Sichuan. In addition, we measure the common insect and disease indexes of the Chinese bee and analyze the parasitism rate, microsporidia infection rate, virus infection rate, and virus infection titer of bee colonies in overwintering and spring breeding. The experimental results show that the anti-insect invasion situation of bees in the six bee farms is significantly different; however, the antimite ability is basically the same.
CITATION STYLE
Liu, C., & Lin, S. (2022). A Pest Intrusion Detection in Chinese Beehive Culture Using Deep Learning. Scientific Programming, 2022. https://doi.org/10.1155/2022/4642995
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