Abstract:

[Significance] In view of the lack of monitoring means of quality influence factors in the production process of characteristic agricultural products with in central and western regions of China, the weak ability of intelligent control, the unclear coupling relationship of quality control elements and the low degree of systematic application, the existing technologies described such as intelligent monitoring of facility environment, growth and nutrition intelligent control model, architecture of intelligent management and control platform and so on. Through the application of the Internet of Things, big data and the new generation of artificial intelligence technology, it provides technical support for the construction and application of intelligent process quality control system for the whole growth period of characteristic agricultural products. [Progress] The methods of environmental regulation and nutrition regulation are analyzed, including single parameters and combined control methods, such as light, temperature, humidity, CO₂ concentration, fertilizer and water, etc. The multi-parameter coupling control method has the advantage of more comprehensive scene analysis. Based on the existing technology, a multi-factor coupling method of integrating growth state, agronomy, environment, input and agricultural work is put forward. This paper probes into the system architecture of the whole process service of quality control, the visual identification system of the growth process of agricultural products and the knowledge-driven agricultural technical service system, and introduces the technology of the team in the disease knowledge Q & A scene through multi-modal knowledge graph and large model technology. [Conclusions and Prospects] Based on the present situation of the production of characteristic facility agricultural products and the overall quality of farmers in the central and western regions of China, it is appropriate to transfer the whole technical system such as facility tomato, facility cucumber and so on. According to the varieties of characteristic agricultural products, cultivation models, quality control objectives to adapt to light, temperature, humidity and other parameters, as well as fertilizer, water, medicine and other input plans, a multi-factor coupling model suitable for a specific planting area is generated and long-term production verification and model correction are carried out. And popularize it in a wider area, making full use of the advantages of intelligent equipment and data elements will promote the realization of light simplification of production equipment, scene of intelligent technology, diversification of service models, on-line quality control, large-scale production of digital intelligence, and value of data elements, further cultivate facilities to produce new quality productivity.

Key words: characteristic agricultural products, multi-factor coupling, intelligent management and control model, multimodal knowledge graph, agricultural large model, new quality productivity