农业知识智能服务技术综述

doi:10.12133/j.smartag.SA202306002

摘要/Abstract

摘要：

［目的/意义］ 农业环境动态多变、动植物生长影响因子众多且互作关系复杂，如何将分散无序信息理解生成生产知识或决策案例是世界性难题。农业知识智能服务技术是应对农业数据低秩化、规则关联度低和推理可解释性差等现状，提升农业生产全过程综合预测和决策分析能力的核心关键。［进展］本文综合分析了感知识别、知识耦合、推理决策等农业知识智能服务技术，构建由云计算支撑环境、大数据处理框架、知识组织管理工具、知识服务应用场景组成的农业知识智能服务平台，提出一种基于知识规则和事实案例相结合的农情解析与生产推理决策方法，构造产前规划、产中管理、收获作业、产后经营等全链条知识智能应用场景。［结论/展望］从农业多尺度农情稀疏特征发现与时空态势识别、农业跨媒体知识图谱构建与自演化更新、复杂成因农情多粒度关联与多模式协同反演预测、基于生成式人工智能的农业领域大语言模型设计、知识智能服务平台与新范式构建等方面对农业知识智能服务技术发展趋势进行总结，对实现农业生产由“看天而作”到“知天而作”转变具有技术支撑作用。

关键词: 农业知识智能服务, 知识耦合, 推理决策, 多模态知识图谱, 农情预警

Abstract:

Significance Agricultural environment is dynamic and variable, with numerous factors affecting the growth of animals and plants and complex interactions. There are numerous factors that affect the growth of all kinds of animals and plants. There is a close but complex 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 internet technology, a vast amount of agricultural information and knowledge is available on the internet. However, due to the lack of effective organization, the utilization rate of these agricultural information knowledge is relatively low.How to analyze and generate production knowledge or decision cases from scattered and disordered information is a big challenge all over the world. Agricultural knowledge intelligent service technology is a good way to resolve the agricultural data problems such as low rank, low correlation, and poor interpretability of 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 information services. 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. Agricultural knowledge intelligent service technologies such as perceptual recognition, knowledge coupling, and inference decision-making are conducted. The characteristics of agricultural knowledge services are analyzed and summarized from multiple perspectives such as industrial demand, industrial upgrading, and technological development. The development history of agricultural knowledge services is introduced. Current problems and future trends are also discussed in the agricultural knowledge services field. Key issues in agricultural knowledge intelligence services such as animal and plant state recognition in complex and uncertain environments, multimodal data association knowledge extraction, and collaborative reasoning in multiple agricultural application scenarios have been discussed. Combining practical experience and theoretical research, a set of intelligent agricultural situation analysis service framework that covers the entire life cycle of agricultural animals and plants and combines knowledge cases is proposed. An agricultural situation perception framework has been built based on satellite air ground multi-channel perception platform and Internet real-time data. Multimodal knowledge coupling, multimodal knowledge graph construction and natural language processing technology have been used to converge and manage agricultural big data. Through knowledge reasoning decision-making, agricultural information mining and early warning have been carried out to provide users with multi-scenario agricultural knowledge services. Intelligent agricultural knowledge services have been designed such as multimodal fusion feature extraction, cross domain knowledge unified representation and graph construction, and complex and uncertain agricultural reasoning and decision-making. An agricultural knowledge intelligent service platform composed of cloud computing support environment, big data processing framework, knowledge organization management tools, and knowledge service application scenarios has been built. Rapid assembly and configuration management of agricultural knowledge services could be provide by the platform. The application threshold of artificial intelligence technology in agricultural knowledge services could be reduced. In this case, problems of agricultural users can be solved. A novel method for agricultural situation analysis 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 of small 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 and knowledge graph to analyze and gather high-level semantic information of cross media content. (3) In response to the difficulties in tracing the origin of complex agricultural conditions and the low accuracy of comprehensive prediction, multi granularity correlation and 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 agricultural data and automatically generate questions through large-scale computing power, solving the problems of user intention understanding and 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 as identification, prediction and decision-making by combining strong algorithms with Big data and super computing power. These could bring important opportunities for large-scale intelligent agricultural production. (5) The construction of knowledge intelligence service platforms and new paradigm of knowledge service, integrating and innovating a self-evolving agricultural knowledge intelligence service cloud platform. Agricultural knowledge intelligent service technology will enhance the control ability of the whole agricultural production chain. It plays a technical support role in achieving the transformation of agricultural production from "observing the sky and working" to "knowing the sky and working". The intelligent agricultural application model of "knowledge empowerment" provides strong support for improving the quality and efficiency of the agricultural industry, as well as for the modernization transformation and upgrading.

Key words: agricultural knowledge intelligent services, knowledge coupling, reasoning decisions, multimodal knowledge graph, early warning of agricultural condition

中图分类号:

S126

赵春江. 农业知识智能服务技术综述[J]. 智慧农业(中英文), 2023, 5(2): 126-148.

ZHAO Chunjiang. Agricultural Knowledge Intelligent Service Technology: A Review[J]. Smart Agriculture, 2023, 5(2): 126-148.

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模型		特点	作物类型	结果
两阶段农业目标检测	Faster R-CNN^［24］	针对背景复杂、多尺度小目标特征检测表现良好	番茄病虫害定位检测	平均识别精度达到85.98%
	改进Faster R-CNN^［25］	采用区域特征聚集改进Faster R-CNN兴趣区域池化层，以降低特征量化误差	小麦锯蝇、小麦蚜、小麦螨	平均精度均值达到81.0%
	MR3P-TS^［26］	扩展了Mask R-CNN中Mask分支，通过计算掩模的多个连通域的面积，识别出了采摘主要部分	茶芽轮廓和采摘点检测	采摘点定位Pr=94.9， Recall=91%
一阶段农业目标检测	改进SSD网络^［27］	融合多尺度卷积核和空洞卷积模块提高特征检测识别能力	原木端面识别	检测精确率达到97%
	GSC-YOLOv3^［28］	将GhostNet作为主干网络，使用空间金字塔池化结构增强特征提取	红花丝检测	平均精度均值达到91.89%
	YOLOv4-GCF^［29］	YOLOv4采用GhostNet作为主干网络，利用注意力机制CBAM提高检测精度	荔枝病虫害检测	平均精度达到89.76%
	YOLOv4-Dense^［30］	YOLOv4结合DenseNet网络将先验框改为符合形状的圆形标记框	樱桃果实检测定位	F₁值达到0.947
	GHTR2-YOLOv5s^［31］	YOLOv5s融合卷积块注意力模块和加权双向特征金字塔网络，具有更高的检测精度	苹果果实病害检测	平均精度均值达到90.9%

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