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Cultivation of tomato plant under hydroponics system in the greenhouse is suitable way to improve fruit quality since it is easier to control environmental parameters. In this system, water and nutrition are two important things for plant to growth. In the tropical area such as Indonesia, air temperature is main constraint in the plant production system. Increasing air temperature inside the greenhouse has positive correlation to the raising temperature of nutrient solution which affected to the ability of the plant to absord the nutrition. The effective way to anticipate increasing of its temperature is by using the cooling system of nutrient solution before circulated to the plant. This paper presented the application of Articificial Neural Network (ANN) to estimate the temperature of nutrient solution which was cooled on day-night time and circulated to the plant. ANN models, called time delay neural network, consist of 3 layers with 4 input nodes and 1 output node. As input model were t (time), Tg(i) (air temperature inside the greenhouse on time i), Tt(i) (temperature of nutrient solution in the tank on time i), Tb(i-1) (temperature of nutrient solution in the plant plots on time i-1) and as output model was Tb(i) (temperature of nutrient solution in the plant plots on time i). The model was developed well with validation result better than heat transfer model previously indicated by coefficient determination (R2) of 0.9498.

Keywords: cooling, nutrient solution, hydroponic, tomato, artificial neural network

Diterima: 15 Juni 2010;Disetujui: 30 juli 2010

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