Non-Invasive measurement of signals transmitted by Plants using Kalman neuro Texture Analysis

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In a protected plant production system such as a plant factory, the control applications have been limited to environmental controls. The feedback control technology for greenhouse environmental factors such as temperature, humidity, radiation intensity, carbon dioxide concentration and so forth has been developed and successfully implemented. Using the technology, plant growth can be optimized or controlled by adjusting environmental factors. Plants normally respond to change of environmental parameters. For example, stomata activity is sensitive to ambient humidity and CO_2 concentration. Plant tissue rigidity is affected by the availability of water in the root zone.Environmental factors should be controlled based upon the response of plants to them. The development of bio-response feedback control system has been a challenging task for plant production engineers and scientists. Another important aspect is the development of non-invasive technology for acquiring information of growi … More ng plants. The practice of using non-invasive measurements in the plants is essential for the bio-response feedback and/or feed-forward control system of a centrifuge phytotron. No non-invasive method for direct measurement of plant water status, for instance, is currently available. One alternative uses an indirect measurement technique. Those who are skilled in growing plants can sense whether their plants are under adequate water conditions or not, from minor changes in the appearance of their plants through their color and tone, before the plants wilt. It may be possible to predict the leaf water potential from the appearance of plants.Changes in appearance of a plant canopy or a community of plants due to the growth reflect tonal variations over the community of plants. The tonal variation can be transformed into pictorial information electronically in retrieval form. Some image features can be related to the tonal characteristics of the plant canopy that also substantially reflect the plant growth status. Machine vision can be used to monitor plants growth. Continuous capturing images of plants during their life cycle allows monitoring and possibly early detection of defects. To evaluate the growth including the health of plants based on image features of plants obtained by machine vision system, an intelligent information processing system is required that will be able to identify the plant growth stage and diagnose symptoms of stress.In order to achieve the aim of developing such a bio-response feedback control system, the primary concern should be to develop an effective and practical technique for extracting image features which can eventually indicate the plant growth status. In this study, the textural analysis and the finite element retina were tested. The test results showed that problems in implementing the textural analysis are that there is too much flexibility to construct the co-occurrence matrix and the construction of the co-occurrence matrix requires extremely long calculation time.The result also showed that extracted finite element features clearly indicate the change in texture of the captured image due to plant growth. The performance of the finite element retina was better than the textural analysis scheme in terms of resolution and processing time. Less