Artificial lighting is a key factor in Closed Production Plant Systems (CPPS). A significant light-emitting diode (LED) technology attribute is the emission of different wavelengths, called light recipes. Light recipes are typically configured in continuous mode, but can also be configured in pulsed mode to save energy. We propose two nonlinear models, i.e., genetic programing (GP) and feedforward artificial neural networks (FNNs) to predict energy consumption in CPPS. The generated models use the following input variables: intensity, red light component, blue light component, green light component, and white light component; and the following operation modes: continuous and pulsed light including pulsed frequency, and duty cycle as well energy consumption as output. A Spearman’s correlation was applied to generate a model with only representative inputs. Two datasets were applied. The first (Test 1), with 5700 samples with similar input ranges, was used to train and evaluate, while the second (Test 2), included 160 total datapoints in different input ranges. The metrics that allowed a quantitative evaluation of the model’s performance were MAPE, MSE, MAE, and SEE. Our implemented models achieved an accuracy of 96.1% for the GP model and 98.99% for the FNNs model. The models used in this proposal can be applied or programmed as part of the monitoring system for CPPS which prioritize energy efficiency. The nonlinear models provide a further analysis for energy savings due to the light recipe and operation light mode, i.e., pulsed and continuous on artificial LED lighting systems.

Nowadays, lifestyle change is one of the problems of the new world economic order, and the procedures of feeding, purchasing, preparation, and the storage of food products, are forcing authorities to establish more rigorous methods concerning the control of food quality and safety. Owing quality in the agro-food sector is a complex and global issue, due to the distance between production and final consumption, as well as the new demands of society on food. Contributing to the bacteria minimization during their path in the supply chain, the objective of this research is the use of an UV-C LED artificial lighting system with emission in continuous light (CL) and two of pulsed light (Mode 1 and Mode 2) for fresh products’ disinfection. A mathematical model is introduced as a reference to establish the equivalence dose of continuous and pulsed UV-C LED irradiation. The doses applied were 5, 15, and 25 mJ cm⁻². The configured parameters per each technique were the irradiance, time also the frequency (500 Hz), and duty cycle (30, 50, and 80%) for Mode 1 and Mode 2. The germicidal effect (GE), energy consumption, and effective germicidal effect (EGE), were evaluated for the different techniques. According to the results, the technique Mode 1 was the best in the GE with 1.06 ± 0.01 and 1.08 ± 0.01 Log reduction by 25 mJ cm⁻² at 30 and 80% duty cycle, correspondingly. The CL and Mode 1 showed an outstanding performance with the EGE. Finally, Mode 1 reduced 11% in energy and the GE is comparable with CL. The pulsed light technique Mode 1 constitutes a powerful method against the microorganism’s destruction and a strategy for saving energy during the treatment. The UV-C LEDs proved to be an excellent alternative in the disinfection of fresh products with pulsed light emission in the real process.