The application of expert system and neural networks in crop growth management system

Author(s):  
Yuan Li ◽  
Wenqing Zhang ◽  
Huiqin Yang
2014 ◽  
Vol 543-547 ◽  
pp. 4161-4164
Author(s):  
Hong Juan Li ◽  
Shu Mei Zhang

Information technology includes neural networks, ontology technology, expert system, and so on, and the growth model can predict and manage growth conditions of fruit trees. The traditional expert system has shortcomings of poor self-learning ability, so the improved expert system is used to perform diagnosis of diseases and insects of fruit tree. Firstly the ontology is used to collect related symptoms of diseases and insects of fruit trees, the expert system and neural network are combined to build the prediction model of diseases and insects of fruit tree, then the conclusions of the diagnostic process are regarded as the input neurons and output neurons of neural networks, and are diagnosed by expert, so the prediction models of disease diagnosis of fruit trees are made. The models can implement the function of expert diagnosis and prediction, and provide technical support and management decision for the growth management of fruit tree, greatly improving the diagnosis efficiency of diseases and insects of fruit tree.


2012 ◽  
Vol 235 ◽  
pp. 329-333
Author(s):  
Xu Ning Liu ◽  
Jing Zhang ◽  
Ya Bin Fan ◽  
Xu Chen

In order to improve the decision performance of expert system, then the growth simulation model based on expert system is proposed. The paper discussed the structure and functions of plant growth model, and analyzed the related techniques and so on, and the structure and functional modules of growth management system are introduced at first, then the growth model of plant is made, the expert system is used to carry out decision-making on the growth of plant, then the visual simulation model is used to simulate the growth management and yield of plant. Research result has shown that the system can perform well and help the managers to provide decisions for the management of plant. Research and development of this system provide technical reference for the development of new agricultural expert system.


2014 ◽  
Vol 513-517 ◽  
pp. 3728-3731
Author(s):  
Wen Qing Zhang

In order to simulate growth and development process of tree, then provide services for production management and scientific research, all kinds of tree growth models are constructed. The paper firstly considers a variety of factors affecting the growth and development of tree, then studies artificial intelligence knowledge such as neural network and expert system, uses the neural expert system to solve the acquisition and management of tree growth parameters, and design and develop tree growth management and expert system based on growth models, the models combine morphogenesis model of tree and knowledge model to provide comprehensive environmental control and management decision-making. Practice has indicated that the growth models of tree can reflect the growth of trees under different physiological and ecological conditions, and visual effect is very good.


2001 ◽  
Vol 27 (2) ◽  
pp. 81-92 ◽  
Author(s):  
Brenda L. Killingsworth ◽  
Michael B. Hayden ◽  
Robert Schellenberger

2018 ◽  
Vol 2 (2) ◽  
pp. 231-247
Author(s):  
Safwan Hasoon ◽  
Fatima Younis

the development in computer fields, especially in the software engineering, emerged the need to construct intelligence tool for automatic translation from design phase to coding phase, for producing the source code from the algorithm model represented in pseudo code, and execute it depending on the constructing expert system which reduces the cost, time and errors that may occur during the translation process, which has been built the knowledge base, inference engine, and the user interface. The knowledge bases consist of the facts and the rules for the automatic transition. The results are compared with a set of neural networks, which are Back propagation neural network, Cascade-Forward network, and Radial Basis Function network. The results showed the superiority of the expert system in automatic transition process speed, as well as easy to add, delete or modify process for rules or data of the pseudo code compared with previously mentioned neural networks.


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