The exclusion of infrared radiation contained in the solar radiation by greenhouse covering materials is expected to be an effective method to promote greenhouse culture during a high temperature period and cut down the cooling load of green-houses in the daytime. So, authors are investigating on the thermal characteristics in a greenhouse covered with a new selective absorber of infrared radiation which has been commercialized.
In the previous paper, the effect of the covering material on inside air and soil surface temperatures was mainly reported. In this report, the experimental results on the effect of the covering material on leaf temperature and growth of leaf vegetable are shown in comparison with a normal clear glasshouse.
The main results are as follows :
1) The effect on suppression of temperature rise in lettuce leaf under hydroponic was relatively large and increased with increasing solar radiation
2) Due to the suppression of an excessive rise in leaf temperature by using the covering material, the effect on the growth of leaf vegetables was also large except a low temperature period of December.

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Possibility of completely-controlled plant factory using laser as light source for photosynthesis is discussed in detail. It is shown that visible laser diodes (LD) together, according to circumstances, with second-harmonic generation (SHG) technique of total efficiency nearly 30% can compete with commonly used high-pressure sodium lamp. This level may be attainable both technically and economically in some future.

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A new optimal control technique for ventilation in a fruit storage factory is proposed. The method is composed of two steps. In the first step, static model for calculating the evapotranspiration of orange (Iyokan) fruit is first constructed by using neural networks, and then the absolute humidity in the storage factory is calculated. In the second step, the dynamic model of absolute humidity as affected by ventilation is constructed by using neural networks, and then optimal value (6-step on-off ventilation times which minimize the deviation between the current values and the setpoint of the absolute humidity) is sought by using genetic algorithms. A three-layer neural network was effective for the identification of the non-linearity between temperature and evapotranspiration of fruit. The genetic algorithm allowed the optimal value to be quickly sought from the model simulation when the crossover and mutation rates had high values. Thus, the hybrid control technique involving neural networks and genetic algorithms is effective for the optimal control of environmental factors in the fruit storage factory.

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An automatic harvesting system for leaf vegetables was developed and its performance was tested in a plant growing factory. In order to apply to the trough-type hydroponics, the system consisted of a trough joint, a guideway for the carring-strip, the slice cutter, belt conveyors, a winder of the carring-strip, and so on. The harvesting method was following;
1. The carring-strip which was planted vegetables is moved by the winder.
2. The carring-strip and vegetables are inclined by the guideway.
3. Roots of vegetables are cut off with planting-pots by the slice cutter.
4. Therefore, harvestries fall and are carried by belt conveyors.
This system could harvest 1800 planting-pot·hr^-1. The result of practical test suggest that the efficiency of the system was 3-5 times as fast as harvesting speed by human labor.

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In a plant factory, optimal control technique for growth optimization of plants is severely required from a view of cost-performance. The present works is attempts to apply the intelligent control technique including neural networks and genetic algorithms to the optimal control of the growth of spinach plants in hydroponics. The optimal control problem is to decide three set points of light intensity, temperature during the daytime and temperature during the nighttime, which maximize the fresh weight of the plants. In the system, responses of the fresh weight as affected by light intensity and temperatures during day and night are first identified using the neural network and then optimal values (three set points) are searched through the simulation of the identified model using the genetic algorithm. A three-layer neural network was effective for the identification of the fresh weight as affected by light intensity, temperature during daytime and temperature during nighttime. The genetic algorithm allowed the optimal value to be quickly sought from the model simulation. Thus, the intelligent control technique involving neural networks and genetic algorithms is quite useful for the control for growth optimization of plants in a plant factory.

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