In recent years, with the continuous innovation of the Internet of Things technology, the image processing technology in the Internet of Things technology has become more and more mature. Automated pig raising will become the mainstream pig raising technology, making the research of image processing technology in the intelligent pig breeding face a change. It has become more and more important. The traditional pig raising model cannot provide a suitable growth and development environment for the pigs. The pigs are disturbed by diseases and environmental discomforts during the growth process, which increases the mortality of the pig breeding process and cannot provide consumers with a guarantee. In order to solve the problem of unfavorable factors during the growth of live pigs, this article uses image processing technology to analyze data through images obtained through automated monitoring and management, uses the system to conduct intelligent, digitized, and standardized management of pig breeding data, and reports to the corresponding. The control module issues instructions to improve the corresponding environmental information and realize the intelligent management of pig breeding. This article will use image processing technology to monitor the growth of pigs in intelligent pig breeding. Studies have shown that the use of image processing technology to realize the intelligent management of pig breeding can help pig farms to carry out manual management to improve production efficiency and management efficiency. The management mode of the pig industry has changed from fuzzy to refined. The breeding cost of pig farms and a lot of manpower and material resources should be reduced, reducing the probability of pigs getting sick and the impact of the environment, reducing the mortality of pigs, improving their economic benefits, and providing consumers with a strong guarantee.

There is an urgent need of developing grape picking robot with intelligent recognition function due to the decrease of grape picking workers’ population. Acquiring the 3D information of picking coordinate is the key process of constructing intelligent picking equipment. In this paper, based on SSD MobileNet neural network model, transfer learning and central deviation angle method were used to realize the positioning of picking coordinate points of facility cultivation grape by machine vision. After testing 720 fruit labels, 633 stem labels and 603 leaf labels labelled by pretreatment, the general precision was 79.5%, which was close to the inherent accuracy of the original model before transfer learning.

Precisely calling chromatin loops has profound implications for further analysis of gene regulation and disease mechanisms. Technological advances in chromatin conformation capture (3C) assays make it possible to identify chromatin loops in the genome. However, a variety of experimental protocols have resulted in different levels of biases, which require distinct methods to call true loops from the background. Although many bioinformatics tools have been developed to address this problem, there is still a lack of special introduction to loop-calling algorithms. This review provides an overview of the loop-calling tools for various 3C-based techniques. We first discuss the background biases produced by different experimental techniques and the denoising algorithms. Then, the completeness and priority of each tool are categorized and summarized according to the data source of application. The summary of these works can help researchers select the most appropriate method to call loops and further perform downstream analysis. In addition, this survey is also useful for bioinformatics scientists aiming to develop new loop-calling algorithms.

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