Root zone temperature prediction of NFT hydroponic lettuce based on GA-BP

Under the mode of Nutrient Film Technique（NFT）, crops are more sensitive to environmental changes. In order to ensure reasonable environmental conditions in the root zone of crops, it is necessary to accurately regulate the temperature in the cultivation pipeline, so as to effectively improve the quality of hydroponic lettuce and reduce the overall energy consumption of greenhouse environmental regulation. In this study, Genetic Algorithm（GA） was used to optimize the input weights and thresholds of the BP neural network model. Taking a single NFT cultivation tank as the research object, temperature prediction models were constructed for different monitoring regions in the root zone of the tank. The Convolutional Neural Network（CNN） models are compared with the standard BP neural network and convolutional neural network. The results show that compared with the standard BP and CNN neural network models, the root mean square error of the GA-BP prediction model is reduced by 0. 82 and 0. 42, the average absolute error is reduced by 0. 54and 0. 25, and the absolute coefficient is increased by 0. 08 and 0. 03, respectively. This method improves the accuracy of NFT root-zone temperature prediction model based on BP neural network algorithm and provides a reliable basis for the precise control of root-zone temperature.