Physiomechanic Control of Cell Division Rate at Plant Root Tip

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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