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.

In the field of robotics, forward kinematics is an activity that allows finding a mathematical model for the resulting position in the final effector based on the robot joints position, a popular alternative for determining this model is defined by the Denavit Hartenberg convention, nevertheless, this method requires knowledge about linear algebra and three-dimensional spatial kinematics. Machine learning uses specific computational methodologies to solving similar problems in several areas, so it could be a viable answer for automatic determining of forwarding kinematics. In this work we propose the use of genetic programming as a machine learning algorithm for finding the forward kinematics of a 2 degrees of freedom robot, getting a satisfactory outcome obtaining a satisfactory result with blocks that describe the expected solution, validating the capacity of the genetic programming in order to validate this algorithm for later work with more complex robots.

There are several tables with important data used in the calculus of different processes like machining tables, friction tables and thermodynamics processes tables, or as it is explored in this paper, the description of saturated water and steam table. We propose the generation of equations for describing the entire behavior of numerical values in a table using Genetic Programming (GP), when table data describes the variable behavior of a dependent function. This obtained equations simplify the calculus process without requiring several tables and allowing to work when tables are not available for a desired value of an independent variable, a common situation in thermodynamics. In this case it is tested the proposed algorithm for synthesizing the saturated water and steam table.

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Vision sense is achieved using cells called rods (luminosity) and cones (color). Color perception is required when interacting with educational materials, industrial environments, traffic signals, among others, but colorblind people have difficulties perceiving colors. There are different tests for colorblindness like Ishihara plates test, which have numbers with colors that are confused with colorblindness. Advances in computer sciences produced digital assistants for colorblindness, but there are possibilities to improve them using artificial intelligence because its techniques have exhibited great results when classifying parameters. This chapter proposes the use of artificial neural networks, an artificial intelligence technique, for learning the colors that colorblind people cannot distinguish well by using as input data the Ishihara plates and recoloring the image by increasing its brightness. Results are tested with a real colorblind people who successfully pass the Ishihara test.

The use of closed growth environments, such as greenhouses, plant factories, and vertical farms, represents a sustainable alternative for fresh food production. Closed plant production systems (CPPSs) allow growing of any plant variety, no matter the year’s season. Artificial lighting plays an essential role in CPPSs as it promotes growth by providing optimal conditions for plant development. Nevertheless, it is a model with a high demand for electricity, which is required for artificial radiation systems to enhance the developing plants. A high percentage (40% to 50%) of the costs in CPPSs point to artificial lighting systems. Due to this, lighting strategies are essential to improve sustainability and profitability in closed plant production systems. However, no tools have been applied in the literature to contribute to energy savings in LED-type artificial radiation systems through the configuration of light recipes (wavelengths combination. For CPPS to be cost-effective and sustainable, a pre-evaluation of energy consumption for plant cultivation must consider. Artificial intelligence (AI) methods integrated into the prediction crucial variables such as each input-variable light color or specific wavelengths like red, green, blue, and white along with light intensity (quantity), frequency (pulsed light), and duty cycle. This paper focuses on the feature-selection stage, in which a regression model is trained to predict energy consumption in LED lights with specific light recipes in CPPSs. This stage is critical because it identifies the most representative features for training the model, and the other stages depend on it. These tools can enable further in-depth analysis of the energy savings that can be obtained with light recipes and pulsed and continuous operation light modes in artificial LED lighting systems.

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