Task guidance type smart agricultural planting expert system

Abstract

The invention discloses a task guidance type smart agricultural planting expert system. A knowledge obtaining module builds a knowledge base by obtaining agricultural planting knowledge, a knowledge base management module sets confidence for each rule type knowledge, and the inference accuracy of uncertain events is improved; The reasoning task is generated; An improved Rte network mode matching algorithm is adopted; knowledge stored in the knowledge base is inquired and a rule required by the reasoning task is subjected to matching and activating operation in the knowledge base and variablesin a variable library and the external user data are combined, reasoning of the reasoning task is completed according to the confidence coefficient of the activated rule, and finally a structured production task is generated for guiding a user to carry out agricultural production activities to realize scientific planting. By adopting the embodiment of the invention, the method can be extended to model establishment and reasoning of other crops, and after a rule base and a knowledge base are continuously huge, the method has higher matching speed, reasoning efficiency and model generalization.

Description

technical field [0001] The invention relates to the field of agricultural technology, in particular to a task-guided intelligent agricultural planting expert system. Background technique [0002] At present, a lot of work has been done in theoretical research and system design in smart agriculture, including expert system-based pest diagnosis, temperature control, water and fertilizer control, crop growth models, and knowledge representation. However, the expert system is too targeted. Most of the existing agricultural expert systems only aim at a specific problem, such as pest control, rice scientific planting, etc. There is no general agricultural expert system for modeling the general crop growth process. Due to the wide variety of single crops, such as nearly 20 main varieties of wheat, the generalization of the model is difficult, and the differences in different geographical environmental factors are large. If the model is not automatically corrected according to envir...

AI Technical Summary

Problems solved by technology

However, the expert system is too targeted. Most of the existing agricultural expert systems are only for a specific problem, such as pest control, rice scientific planting and other issues. There is no general agricultural expert system for modeling the general crop growth process.

Due to the variety of single crops, such as nearly 20 main varieties of wheat, the generalization of the model is difficult, and the differences in different geographical environmental factors are large. If the model is not automatically corrected according to environmental factors, it is difficult to promote

[0003]In addition, the existing agricultural expert system is not ideal for uncertainty management and control in reasoning, which has a great impact on the accuracy of reasoning and decision-making, and has a large Most systems are only based on the current data, find out what is wrong with the crops in the process of growing, and do not give a solution

However, most of the user groups in this field are agricultural growers with limited knowledge, and it is difficult to improve production according to the conclusions given by the existing expert system, making it difficult to apply the existing agricultural expert system to actual production

Images