农业知识智能服务技术综述

Significance]Agricultural environment is dynamic and variable, with numerous factors affecting the growth of animals and plantsand complex interactions. There are numerous factors that affect the growth of all kinds of animals and plants. There is a close butcomplex correlation between these factors such as air temperature, air humidity, illumination, soil temperature, soil humidity, diseases,pests, weeds and etc. Thus, farmers need agricultural knowledge to solve production problems. With the rapid development of internettechnology, a vast amount of agricultural information and knowledge is available on the internet. However, due to the lack of effectiveorganization, the utilization rate of these agricultural information knowledge is relatively low.How to analyze and generate productionknowledge or decision cases from scattered and disordered information is a big challenge all over the world. Agricultural knowledgeintelligent service technology is a good way to resolve the agricultural data problems such as low rank, low correlation, and poor interpretabilityof reasoning. It is also the key technology to improving the comprehensive prediction and decision-making analysis capabilities of the entire agricultural production process. It can eliminate the information barriers between agricultural knowledge, farmers,and consumers, and is more conducive to improve the production and quality of agricultural products, provide effective informationservices.[Progress]The definition, scope, and technical application of agricultural knowledge intelligence services are introduced in this paper.The demand for agricultural knowledge services are analyzed combining with artificial intelligence technology. Agriculturalknowledge intelligent service technologies such as perceptual recognition, knowledge coupling, and inference decision-making areconducted. The characteristics of agricultural knowledge services are analyzed and summarized from multiple perspectives such as industrialdemand, industrial upgrading, and technological development. The development history of agricultural knowledge services isintroduced. Current problems and future trends are also discussed in the agricultural knowledge services field. Key issues in agriculturalknowledge intelligence services such as animal and plant state recognition in complex and uncertain environments, multimodal dataassociation knowledge extraction, and collaborative reasoning in multiple agricultural application scenarios have been discussed. Combiningpractical experience and theoretical research, a set of intelligent agricultural situation analysis service framework that coversthe entire life cycle of agricultural animals and plants and combines knowledge cases is proposed. An agricultural situation perceptionframework has been built based on satellite air ground multi-channel perception platform and Internet real-time data. Multimodalknowledge coupling, multimodal knowledge graph construction and natural language processing technology have been used to convergeand manage agricultural big data. Through knowledge reasoning decision-making, agricultural information mining and earlywarning have been carried out to provide users with multi-scenario agricultural knowledge services. Intelligent agricultural knowledgeservices have been designed such as multimodal fusion feature extraction, cross domain knowledge unified representation and graphconstruction, and complex and uncertain agricultural reasoning and decision-making. An agricultural knowledge intelligent serviceplatform composed of cloud computing support environment, big data processing framework, knowledge organization managementtools, and knowledge service application scenarios has been built. Rapid assembly and configuration management of agriculturalknowledge services could be provide by the platform. The application threshold of artificial intelligence technology in agriculturalknowledge services could be reduced. In this case, problems of agricultural users can be solved. A novel method for agricultural situationanalysis and production decision-making is proposed. A full chain of intelligent knowledge application scenario is constructed.The scenarios include planning, management, harvest and operations during the agricultural before, during and after the whole process.[Conclusions and Prospects]The technology trend of agricultural knowledge intelligent service is summarized in five aspects.(1) Multi-scale sparse feature discovery and spatiotemporal situation recognition of agricultural conditions. The application effects ofsmall sample migration discovery and target tracking in uncertain agricultural information acquisition and situation recognition are discussed.(2) The construction and self-evolution of agricultural cross media knowledge graph, which uses robust knowledge base andknowledge graph to analyze and gather high-level semantic information of cross media content. (3) In response to the difficulties intracing the origin of complex agricultural conditions and the low accuracy of comprehensive prediction, multi granularity correlationand multi-mode collaborative inversion prediction of complex agricultural conditions is discussed. (4) The large language model(LLM) in the agricultural field based on generative artificial intelligence. ChatGPT and other LLMs can accurately mine agriculturaldata and automatically generate questions through large-scale computing power, solving the problems of user intention understandingand precise service under conditions of dispersed agricultural data, multi-source heterogeneity, high noise, low information density,and strong uncertainty. In addition, the agricultural LLM can also significantly improve the accuracy of intelligent algorithms such asidentification, prediction and decision-making by combining strong algorithms with Big data and super computing power. These couldbring important opportunities for large-scale intelligent agricultural production. (5) The construction of knowledge intelligence serviceplatforms and new paradigm of knowledge service, integrating and innovating a self-evolving agricultural knowledge intelligence servicecloud platform. Agricultural knowledge intelligent service technology will enhance the control ability of the whole agriculturalproduction chain. It plays a technical support role in achieving the transformation of agricultural production from "observing the skyand working" to "knowing the sky and working". The intelligent agricultural application model of "knowledge empowerment" providesstrong support for improving the quality and efficiency of the agricultural industry, as well as for the modernization transformationand upgrading.