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The growth and development process and yield formation of crops are greatly affected by fertilizers. The monitoring and diagnosis technology of crop growth based on machine vision is one of the methods of near-ground remote sensing monitoring.
The high-quality and clear digital images of machine vision can not only easily evaluate the seasonal changes of crop growth and development, but also provide growth information and nutritional status diagnosis of the crop in a real-time, efficient, fast, accurate, automatic, and non-destructive way, which plays an extremely important role in the information-based precision agricultural production. It can also help farmers to take agronomic measures (fertilization, irrigation, farming, harvesting, and control of disease, insects, grass, rodent, etc.) in a timely manner, thereby improving crop yield and quality.
In order to reduce the work intensity of the whole process of grafted seedling cultivation and improve the survival rate and growth quality of grafted seedlings, the fully automatic cultivation of grafted seedlings is the trend of future development.
The automatic cultivation of grafted seedlings includes several steps of precise directional automatic sowing, automatic seedling raising, automatic seedling supply, automatic grafting, automatic transplanting of grafted seedlings, and automatic management of greenhouse. There are a lot of researchers at each stage. Among them, the automatic transplanting of grafted seedlings mainly completes the process of taking the seedlings from the grafting machine and then planting the seedlings on the plug tray.
The transplanting effect generally depends on the accuracy and stability of the hole positioning and the design of the end effector. Research on the location of holes based on machine vision in the transplanting process is a current research hotspot.
China is the main origin of tea and is one of the countries with the largest tea cultivation, consumption, and export in the world.
At present, the contradiction between tea picking and labor in China has become a bottleneck in the development of the tea industry, and it is imperative to accelerate the development of the mechanization of tea picking. Using mechanized operations to replace labor can not only reduce costs but also improve the quality of tea picking and production efficiency.
The application of machine vision in tea identification and navigation of tea picking machines has boosted the development of the tea industry. The method of using a computer vision system to identify tea tree buds and realize positioning picking can not only ensures the integrity of leaves, but also fully automates the whole picking process, saving a lot of manpower and material resources, but the recognition efficiency of machine vision needs to be improved.
The application of machine vision in agriculture has laid the foundation for precision agriculture and automation of agricultural production, which not only helps to liberate labor but also helps to improve the quality and yield of crop products.
In addition, the three-dimensional reconstruction of the plant growth process is a research hotspot at home and abroad, and machine vision system technology is an indispensable and important link in the process. Plants have a long growth cycle. Thanks to the 3D reconstruction technology, the development, and growth process of the structure of crops in virtual space can be simulated and displayed in 3D images. It can not only visually and accurately present the three-dimensional growth process of plants, but also predict the growth of plants, providing an efficient and convenient experimental method for biological breeding and seedling raising.
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