| Co-Investigator(Kenkyū-buntansha) |
FUKUYAMA Toshio EHIME UNIVERSITY,DEPT.OF AGRICULTURE,ASSISTANT PROFESSOR, 農学部, 助教授 (90036351)
YAMASHITA Jyun EHIME UNIVERSITY,DEPT.OF AGRICULTURE,ASSISTANT PROFESSOR, 農学部, 助教授 (40036405)
MORIMOTO Tetsuo EHIME UNIVERSITY,DEPT.OF AGRICULTURE,ASSISTANT PROFESSOR, 農学部, 助教授 (50127916)
NISHINA Hiroshige EHIME UNIVERSITY,DEPT.OF AGRICULTURE,ASSISTANT PROFESSOR, 農学部, 助教授 (70134509)
NONAMI Hiroshi EHIME UNIVERSITY,DEPT.OF AGRICULTURE,ASSISTANT PROFESSOR, 農学部, 助教授 (00211467)
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| Research Abstract |
In recent years, there has been great interest in plant factory which enables us to improve the complicated traditional control system for plant production. For the control of such complex systems as plant production systems, a computer-based control system based on artificial intellgence is more suitable than traditional mathematical control system. For multi tasks in cultivation, a computer network system composed of several computers is also necessary. In this study, a computer integrated system (CIS) composed of different several types of computers was developed in order to realize the systematization of plant production system and its efficient control in agriculture.
(1) A computer integrated system (CIS) composed of workstation, artificial intelligence-typed computer and several personal computers, which are connected with local area network (LAN), was developed.
(2) The use of image processing allows a pattern recognition of fruit shape, diagnosis of blossom-end rot and of quality judgment of tomatoes to be efficiently implemented. An effective way for transmitting the image data of plants, which have a very large data, was examined. The completion of image data shows a good transmission performance.
(3) A new intelligent control system combining with neural networks and genetic algorithms was developed.In the method, the physiological processes of a plant, as affected by environmental factors, are at first identified using neural networks and then optimal setpoints of environmental factors are searched for through simulation of the identified model using genetic algorithms. This control system was applied to an actual tomato cultivating system. The control results showed a good vegetative growth and reproductive growth.
(4) Thus, a CIS for plant production developed in this study was shown to be very effective on the optimal control of such a complex and uncertain system as plant cultivating system.
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