Nobuo Honami

Inverse Technique for Analysis of Convective Heat Transfer over the Surface of Plant Culture Vessel

A practical method to determine the forced convective heat transfer coefficient over the plant culture vessel was developed. A finite element neural network inverse technique was used to determine parameter values of the Nusselt equation, which, in turn, is needed to calculate the forced convective heat transfer coefficient. The values of the forced convective heat transfer coefficient obtained by the proposed method were then used for the finite element calculation. Thereby, the temperature distribution inside a plant culture vessel was simulated. Agreement was obtained between the finite element results and experimentally measured temperature distribution inside the plant culture vessel.

Change of Ascorbic Acid Level after Grafting of Tomato Seedlings

Grafting is an easy way to produce a new seedling, which can tolerate against various stresses. During the acclimation after grafting, however, the seedlings still suffer a severe water stress. It is well known that water stress produces active oxygen to oxidize ascorbic acid. The concentration of ascorbic acid in the leaves was analyzed by HPLC equipped with an electrochemical detector. The column used was SP-120-5-ODS-BP (DAISO, JAPAN) and elution was performed with 0.1 ᴍ phosphate buffer, pH 3.0. After grafting the seedlings were acclimated under a 6-hr light/dark regimen. The content of ascorbic acid increased gradually during 2 days compared with control. The ascorbate peroxidase showed about constant activity, so the increase of ascorbic acid may be due to its requirement to cure the grafting

Shape identification using a charge simulation retina model

Investigations were conducted to explore the feasibility of a prototype charge simulation retina machine vision system to identify shape and size, when different three-dimensional objects were arbitrarily located in the vision field of the retina. The system consisted of a light source, light beam conditioner, artificial retina installed with photo sensors, data transfer unit, and a computer installed with analogue to digital converter peripherals. The retina was used to acquire image features for regular prisms. The features were transferred to a charge simulation retina model that was identical to the prototype retina and were compressed using a charge simulation method (CSM) algorithm by computing output signals at work cells located in the retina model. With these signals, neural networks were trained to classify each image sample, to identify shape and size. The results showed that object displacement, especially for locations beyond a circle with a radius one-tenth that of the retina and measured from the centre of the base of the retina, significantly affected shape and size classification performance. Despite this, overall shape and size classification rates of 75% and above were obtained when the retina discriminated between different prisms. The results indicate that it is feasible for the charge simulation retina based on the CSM algorithm to identify three-dimensional shapes.

Acclimation Environment Control After Grafting - A New Acclimation Method -

Abstract The production system for grafted seedlings consists of mainly three parts. One is the growing process, another one is the grafting process, and the last one is the acclimation process. The acclimation process is very important for grafted seedlings because they are seriously injured. The qualities of seedlings and the management of farmers and seedling producers depend upon the acclimating technologies. Now, we are trying to develop such a grafting robot system which satisfies the conditions in the view of plant histological and physiological standpoints. In this study we show a new acclimation method on the basis of high technologies.

Monitoring plant leaf orientation using neural network

Abstract The development of plants greatly depends upon gravity for the direction of growth of their organs. It is imperative to conduct experiments under space environment to determine the effect of gravity on plant development The three-layered neural networks with the kalman filter as a learning algorithm were used. To generate the training data, the clinorotation of 0, 2. 10 and 20 rpm were selected to measure the plant width and height on daily basis alter transplanting on the clinostat Twenty-eight sets of training data comprising of clinorotation, plant height and plant width were used The training was terminated after iterative calculation of 500 times at of

Identification of Microgravity Role in Plant Growth

Abstract The centrifuge arm of Spacetron Junior had a radius of 30 cm and it rotated at 5.5 and 2 rpm to create centrifugal forces in terms of gravity force equal to 1.00×10 -2 - and 1.34×10 -3 -G, was 3 % more and 19 % less, respectively. The fresh weight of plants under microgravity conditions of 1.00×10- and 1.34×10 -3 -G was 3 % more and 19 % less, respectively, than the plants under control condition. The leaf area and fresh weight were 9 and 23 % more, respectively, for the plant under 1.00×10 -2 -G than that plant under 1.34×10 -3 -G. Since the results under the different gravity levels contradict, it is difficult to make far-reaching conclusions.

Identification of a Wilting Behavior of a Tomato Seedling for an Acclimation System of Grafted Seedlings

Abstract It is important to understand the characteristics of grafted seedlings during the acclimation process, especially the moisture content inside the scion, which depends on the balance between transpiration and supply from the stock. The joining rate between the scion and the stock can be done effectively by measuring the wilting rate of a grafted seedling. The wilting behavior of the seedlings was non linear. An attempt was made to model this behavĩor after the balance equation of a spring-mass system, using a synthetic spring coefficient as the variable. The wilting behaviors of different seedlings were successfully identified.

Shape Identification Using a Charge Simulation Retina Model

Abstract A model of a charge simulation retina was developed to distinguish different shapes and sizes. The retina consisted of sensory and work cells. Signals at work cells were computed based on images generated by different 3-D shapes and sizes, arbitrarily located in the field of view of the retina, and the stimuli applied on sensory cells. Using neural networks and based on the signals, overall classification rates of 73% for both shape and size were obtained. Object displacement affected the performance of the retina, especially for locations beyond 0.1R (R is the radius of retinas base). Hence, the retina can identify to a reasonable degree different 3-D shapes and sizes, in spite of arbitrarily locating the shapes in its field of view. However, it is necessary that the centres of area of the object and the retina base be within a 10% error to minimise identification errors.

Identification of Shape by a Charge Simulation Retina Model

Abstract A model of a charge simulation retina was developed to distinguish different shapes and sizes. Sensory cells were evenly distributed on the retina to acquire the image information while work cells were appropriately located in the retina to yield distinct output signals. Signals at the work cells were computed based on images generated by different 3-D shapes which were arbitrarily located in the retina’s vision field, and the stimuli applied on the sensory cells. Using neural networks and based on the signals, overall classification rates of 73% for both shape and size were obtained. Hence, it is feasible for the retina to identify different 3-D shapes and sizes, in spite of arbitrarily locating the shapes in the retina’s vision field.

Application of a Finite Element Neural Network Technique in Predicting Thermal Characteristics of Heat Equivalent Mass

Abstract A heat equivalent mass was developed to represent a plantlet in a thermal system model of a culture vessel. The heat equivalent mass of the plantlet can generate or absorb heat in the numerical system. The location of the heat source and the amount of generated heat from the heat equivalent mass were predicted by a neural network. Results show that the neural network can be used to predict thermal characteristics of the heat equivalent mass based on the finite element model.