Rice disease prediction and diagnosis method based on knowledge graph

Abstract

The invention provides a rice disease prediction and diagnosis method based on a knowledge graph. The rice disease prediction and diagnosis method is realized through the steps of rice disease knowledge extraction, rice disease knowledge representation, rice disease knowledge fusion, rice disease knowledge storage, rice disease knowledge prediction and diagnosis and the like. According to the method, the influence of a large amount of various data and various uncontrollable factors in the field of rice is eliminated, so that the accuracy and diversity of prediction and control are ensured; andmeanwhile, the decision of human subjective factors for expert judgment is avoided, so that the prediction and prevention are more effective and accurate.

Description

technical field [0001] The invention relates to the technical field of rice disease prediction and diagnosis, in particular to a rice disease prediction and diagnosis method based on a knowledge map. Background technique [0002] my country is a disease-prone country. For a long time, disease has been the main problem affecting rice yield. Effective prevention and control of disease development is of great significance to increase rice yield. In the process of rice planting, planters will encounter many difficult problems and need specialized agricultural experts to guide them. Due to the limited number of agricultural experts and constraints of time and space, face-to-face guidance cannot be fully realized. In the face of huge, complex and diverse agricultural information, the traditional expert system can no longer meet the needs of the majority of planters, so there is a higher demand for the system, that is, to obtain the information they need quickly, briefly and accura...

AI Technical Summary

Problems solved by technology

In the process of rice planting, planters will encounter many difficult problems and need specialized agricultural experts to guide them. Due to the limited number of agricultural experts and constraints of time and space, face-to-face guidance cannot be fully realized

In the face of huge, complex and diverse agricultural information, the traditional expert system can no longer meet the needs of the majority of planters, so there is a higher demand for the system, that is, to obtain the information you need quickly, briefly and accurately

[0003] Some rice disease diagnosis systems that have been developed so far have played a positive role in the diagnosis and control of rice diseases. However, due to the weak semantic relevance of knowledge and the difficulty in sharing information, their practical application value is not high.

With the development of agricultural science and technology, the ordinary expert system can no longer meet the requirements of farmers' friends; the traditional expert system only uses rule representation knowledge to analyze the main diseases to establish a disease expert knowledge base when extracting, but users cannot input symptoms by themselves. At the same time, when the user has multiple symptoms, the reliability of the diagnosis result is not high; the problem still exists in the expert system in the prior art is that some only target a certain disease or pest of a certain crop, and some only predict a certain A disease of crops for which comprehensive disease coverage cannot be achieved

Therefore, when planters use expert systems to guide disease prevention and control, it is impossible for users to buy as many expert systems as there are diseases in crops, and the cost is high; at the same time, a large number of systems need to be operated, resulting in cumbersome operations and low efficiency

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