Development of a Neural Network-Based Controller for a Greenhouse Irrigation System at Laboratory Scale

Abstract

Water scarcity and inefficient irrigation practices are major challenges for modern protected agriculture systems. This study designs, implements, and experimentally validates a neural network-based irrigation control strategy in an industrial programmable logic controller (PLC) for a drip irrigation system operating in a laboratory-scale micro-tunnel greenhouse. The objective is to evaluate the real-time performance of an intelligent controller under practical operating conditions and to quantify its impact on water use efficiency and crop growth compared to a conventional on–off strategy. The neural network is trained using 1039 data samples, divided into training (70%), validation (15%), and test (15%) datasets, and is implemented in the PLC to regulate soil moisture through a proportional valve. Experimental validation is carried out over 67 days using a serrano chili pepper (Capsicum annuum L.) crop. Both controllers operate simultaneously under identical environmental and operating conditions. Performance is evaluated using soil moisture stability metrics, including mean squared error (MSE), mean absolute error (MAE), and standard error (SE), water consumption, and crop growth indicators prior to harvest. Results show that the neural network controller achieves higher soil moisture regulation accuracy (MSE = 3.2159%, MAE = 0.7560%, SE = 0.00001687%) and reduces the average daily water consumption per plant by 50.18% compared with the on–off controller. In addition, the absolute growth rate increases by 26.42%, with statistically significant differences. These results demonstrate that neural network-based control can be effectively implemented on industrial hardware and provide tangible benefits for water-efficient and precision irrigation systems.