Artificial Neural Network Levenberg-Marquardt Method for Environmental Prediction of Smart Greenhouse

Temperature is an important factor in growing plants up. It affects the transpiration, photosynthesis, metabolic rate, and respiration of plants. Water spinach (Ipomoea Aquatica) can grow optimal at a temperature of about 25°C-30 °C. In the greenhouse, the increase of air temperature can cause the plants die. In this study, Levenberg-Marquardt’s method of artificial neural networks is used to predict the temperature inside of the greenhouse 30 minutes in the future. The system utilizes inside-outside humidity, light intensity, and outside temperature of the greenhouse as the input. The training yields the correlation coefficients of training, validation, and testing with the value of 0.99. These values are utilized to produce a prediction model. Furthermore, a mist maker and fan use the model to decrease the temperature. In addition, Levenberg-Marquardt’s method was compared to the fuzzy method in terms of suppressing the temperature. The result of temperature range inside the greenhouse are in range 25 ± 1,48°C until 30 ± 0,16°C and 25 ± 2,5°C until 30 ± 8,7°C for the former and the latter, respectively.