Hybrid Recurrent Neural Network in Greenhouse Microclimate Prediction

Abstract

This study presents a hybrid recurrent neural network (RNN) approach for greenhouse microclimate prediction, combining a mechanistic model with an Elman network. The research addresses the gap in systematic comparisons between hybrid RNN and feedforward neural network (FFNN) architectures for greenhouse climate forecasting. Different network structures with 1, 2, 3, 5, and 7 hidden layers were evaluated using mean absolute percentage error (MAPE), mean square error (MSE), and coefficient of determination (R2). Results demonstrate that hybrid RNNs significantly outperform FFNNs in predicting indoor temperature, with the 2-hidden-layer configuration achieving the best performance (R2 = 0.897). For relative humidity prediction, both networks showed comparable results. The hybrid RNN with 3 hidden layers exhibited optimal performance during training, while simpler configurations proved more effective during testing. The integration of mechanistic knowledge with neural networks enhances prediction accuracy, providing a reliable tool for greenhouse climate control systems. These findings contribute to smart agriculture by offering an efficient computational approach for microclimate management.