SYSTEMATIZATION OF CULTIVATING PROCESSES BY ARTIFICIAL INTELLIGENCE

近年来，人们对植物工厂产生了浓厚的兴趣，这使我们能够改进复杂的传统植物生产控制系统。对于工厂生产系统等复杂系统的控制，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用。对于多任务的培养，也需要由多台计算机组成的计算机网络系统。为实现农业植物生产系统的系统化和高效控制，研制了一种由不同类型的计算机组成的计算机集成系统（CIS）。(1)研制了由工作站、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），并与局域网（LAN）相连。(2)利用图像处理技术，可以有效地实现番茄果实形状的模式识别、花端腐病的诊断和番茄品质的判断。对数据量非常大的植物图像数据进行了有效的传输。完成的图像数据显示出良好的传输性能。(3)开发了一种新的神经网络与遗传算法相结合的智能控制系统。该方法首先利用神经网络识别受环境因素影响的植物生理过程，然后利用遗传算法对识别出的模型进行仿真，寻找环境因素的最优设定值。该控制系统已应用于实际番茄栽培系统。对照结果显示营养生长和生殖生长良好。(4)因此，本研究开发的植物生产CIS对于植物栽培系统这样一个复杂且不确定的系统的最优控制是非常有效的。

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.