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SYSTEMATIZATION OF CULTIVATING PROCESSES BY ARTIFICIAL INTELLIGENCE

通过人工智能实现栽培过程的系统化

基本信息

批准号：
03404011
负责人：
HASHIMOTO Yasushi
金额：
$ 19.58万
依托单位：
Ehime University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (A)
财政年份：
1991
资助国家：
日本
起止时间：
1991 至 1994
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-03404011/
关键词：
Computer network system Inference by artificial intelligence Effective use of workstation LAN by earthanet Transmission of image data Neural networks Database for cultivation Computer support system コンピュータ・インテグレート・システムコンピュータ・インテグレード・システ

项目摘要

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.

近年来，人们对植物工厂产生了极大的兴趣，这使我们能够改进复杂的传统植物生产控制系统。对于像植物生产系统这样的复杂系统的控制，基于人工智能的计算机控制系统比传统的数学控制系统更适合。对于多任务栽培，还需要多台计算机组成的计算机网络系统。为了实现农业生产系统的系统化和有效控制，本研究开发了一个由不同类型计算机组成的计算机集成系统（CIS）。(1)研制了一个由工作站、人工智能型计算机和多台个人计算机组成的计算机集成系统（CIS），并通过局域网（LAN）进行连接。(2)图像处理的使用允许有效地实现果实形状的模式识别、花端腐烂的诊断和番茄的质量判断。针对植物图像数据量大的特点，研究了一种有效的植物图像数据传输方法。完成的图像数据显示了良好的传输性能。(3)提出了一种神经网络与遗传算法相结合的智能控制系统，该系统首先利用神经网络辨识受环境因素影响的植物生理过程，然后利用遗传算法对辨识出的模型进行仿真，寻找环境因素的最优设定值。该控制系统已应用于实际的番茄栽培系统中。对照结果表明，该品种营养生长和生殖生长良好。(4)因此，在这项研究中开发的CIS植物生产是非常有效的最优控制这样一个复杂的和不确定的系统，植物栽培系统。