Physiomechanic Control of Cell Division Rate at Plant Root Tip

在设施栽培中，已发展出高水平的植物环境控制技术。植物工厂是集约控制环境下保护栽培的一个很好的例子。在农业工程研究领域，与植物生理相关的仪器仪表、测控技术的良好研究成果的显著积累，使我们能够寻求新技术发展的可能性。本研究的总体目标是通过理论和实验研究来开发新的物理控制技术，从而实现对植物生理的直接控制。应用物理力学理论可以达到这一目的。以下是本研究的个别目标。首先利用模糊线性系统理论和disstal控制理论对包括根尖细胞分裂在内的生理系统进行了识别。第二个努力是开发控制技术，可以控制…在水培培养中根尖的细胞分裂率更高。利用水势调控和超声波技术，建立了根尖细胞分裂速率调控系统。这项工作的实际成果是成功地利用神经网络识别了细胞分裂、植物生理和代谢系统。层次神经网络可用于模拟植物生长、光合作用、蒸发蒸腾等生物系统。用于神经元训练的反向传播算法的发展使得使用分层网络来模拟这种非线性系统成为可能。由于系统的复杂性，使用神经网络对此类生物系统进行建模通常需要在网络体系结构中使用大量的层和单元。然而，反向传播算法往往不能达到令人满意的系统识别，因为反向传播算法的最陡下降方案的输出误差最小化特性不适合涉及大量估计参数（突触权重）的问题。采用卡尔曼滤波模型和反向传播模型两种不同的神经网络模型，模拟了营养液温度和浓度变化对萝卜芽生长的影响。少

In protected cultivation, fairy high level of control technology for plant environment has been developed. Plant factory is a good example of protected cultivation under intensive controlled environment. In agricultural engineering field of study, significant accumulation of good study resofts in instrumentation, measurement and control technology in relation to plant physiology enable us to seek possibility of development of new technology. The overall objective of this research was to conduct theoretical and experimental study to develop new physical control technology that can be implemented to control plant physiology directly. The objective can be achieved through application of physiomechanics theory. The followings are individual objectives of this research. The first effort was to Identify the physiological system including cell division of root tip using fuzzy linear system theory and diSSftal control theory. The second effort was to develop control technology that can control … More rate of cell division at root tip In hydroponic cufture. The control system for governing rate of cell division at root tip was developed using water potential control and ultra sonic wave.The actual achievement of this work was to succeeded to identify the system of cell division, plant physiology and metabolism using neural networks. The hierarchical neural network can be used to model biological systems such as plant growth, photosynthesis, evapotranspiration, etc. The development of back-propagation algorithm for neuron training has made h possible to use the layered network for simulating such non-linear systems. Modeling of such biological systems using the neural network often requires large number of layers and units in the network architecture because of the complexity of the system. The back propagation algorithm, however, often fails to achieve satisfactory identification of the system in the sense that output error minimization characteristics of the steepest descent scheme of the back propagation algorithm does not fit the problems involving large number of estimation parameters(synapse weights). Simulation of growth of radish sprouts under influence of changes in temperature and concentration of nutrient solution was attempted by two different neural network models, i. e., Kalman fitter model and back propagation model. Less