An Artificial Neural Network Model for Predicting the Growth of Lettuce Crops in Hydroponic System with Root Zone Cooling
This title has been presented on Friday, December 15, 2023 at 10.20-10.30 GMT+7.
Keywords:
Greenhouse, hydroponics, lettuce, root zone cooling, artificial neural networkAbstract
This title has been presented on Friday, December 15, 2023 at 10.20-10.30 GMT+7.
This research aims to develop an Artificial Neural Network (ANN) model for predicting the growth of lettuce crops in the Nutrient Film Technique (NFT) hydroponic system. The lettuce’s growth is crucial during the cultivation period, as the environment and nutrients significantly influence it. In this research, we use 4 environmental parameters, 2 nutrient parameters, and 3 growth parameters. The model was developed through steps of (1) data identification, (2) data pre-processing, (3) model development, and (4) model validation. A dataset of 1216 entries has been used to develop the model. The result of this study is an ANN model for predicting the growth of lettuce crops composed of 9 input parameters, including air temperature, root zone temperature, solar radiation intensity, air relative humidity, nutrient concentration, nutrient acidity (pH), leaf area, leaf number, and fresh weight. The optimal model structure consists of 9 inputs, 13 hidden layers, and 1 output with 100 iterations. This model exhibits a coefficient of determination (R2) of 0.93 and a root mean square error (RMSE) of 3.72.
