The main goal of control theory is input tracking or system stabilization. Different feedback-computed controlled systems exist in this area, from deterministic to soft methods. Some examples of deterministic methods are Proportional (P), Proportional Integral (PI), Proportional Derivative (PD), Proportional Integral Derivative (PID), Linear Quadratic (LQ), Linear Quadratic Gaussian (LQG), State Feedback (SF), Adaptative Regulators, and others. Alternatively, Fuzzy Inference Systems (FISs) are soft-computing methods that allow using the human expertise in logic in IF–THEN rules. The fuzzy controllers map the experience of an expert in controlling the plant. Moreover, the literature shows that optimization algorithms allow the adaptation of FISs to control different processes as a black-box problem. Python is the most used programming language, which has seen the most significant growth in recent years. Using open-source libraries in Python offers numerous advantages in software development, including saving time and resources. In this paper, we describe our proposed UPAFuzzySystems library, developed as an FISs library for Python, which allows the design and implementation of fuzzy controllers with transfer-function and state-space simulations. Additionally, we show the use of the library for controlling the position of a DC motor with Mamdani, FLS, Takagi–Sugeno, fuzzy P, fuzzy PD, and fuzzy PD-I controllers.

Vertical farming systems offer an efficient solution for sustainable food production in urban areas. However, managing nitrate (NO₃⁻) levels remains a significant challenge for improving crop yield, quality, and safety. This study evaluated the effects of nitrate availability on growth performance, nutrient uptake, and water use efficiency in a vertical hydroponic system that intercropped lettuce (Lactuca sativa) with alfalfa (Medicago sativa). The experiment was conducted in a controlled vertical hydroponic system using Nutrient Film Technique (NFT) channels, with nitrogen levels set at 0, 33, 66, 100, and 133% of the standard concentration. The results indicated that the intercropping treatment with 66% nitrate (IC-N_66%) improved water use efficiency by 38% and slightly increased leaf area compared to the other intercropping treatments. However, the control group, which consisted of a monoculture with full nitrate supply, achieved the highest overall biomass. Ion concentrations, including nitrate, calcium, magnesium, and micronutrients, were moderately affected by the intercropping strategy and nitrate levels. These findings suggest that moderate nitrate input, combined with nitrogen-fixing legumes, can enhance resource efficiency in hydroponic systems without significantly compromising yield. These findings offer a promising framework for incorporating legumes into hydroponic systems, minimizing the need for synthetic inputs while maintaining yield. These results support the use of agroecological intensification strategies in highly efficient soilless systems.

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.

Global Vertical Farming (VF) applications with characteristic Industry 4.0 connectivity will become more and more relevant as the challenges of food supply continue to increase worldwide. In this work, a cost-effective and portable instrument that enables accurate pH measurements for VF applications is presented. We demonstrate that by performing a well-designed calibration of the sensor, a near Nernstian response, 57.56 [mV/pH], ensues. The system is compared to a ten-fold more expensive laboratory gold standard, and is shown to be accurate in determining the pH of substances in the 2–14 range. The instrument yields precise pH results with an average absolute deviation of 0.06 pH units and a standard deviation of 0.03 pH units. The performance of the instrument is ADC-limited, with a minimum detectable value of 0.028 pH units, and a typical absolute accuracy of ±0.062 pH units. By meticulously designing bias and amplification circuitry of the signal conditioning stage, and by optimizing the signal acquisition section of the instrument, a (minimum) four-fold improvement in performance is expected.