One of the main challenges agricultural greenhouses face is accurately predicting environmental conditions to ensure optimal crop growth. However, the current prediction methods have limitations in handling large volumes of dynamic and nonlinear temporal data, which makes it difficult to make accurate early predictions. This paper aims to forecast a greenhouse’s internal temperature up to one hour in advance using supervised learning tools like Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks combined with Long-Short Term Memory (LSTM-RNN). The study uses the many-to-one configuration, with a sequence of three input elements and one output element. Significant improvements in the R², RMSE, MAE, and MAPE metrics are observed by considering various combinations. In addition, Bayesian optimization is employed to find the best hyperparameters for each algorithm. The research uses a database of internal data such as temperature, humidity, and dew point and external data such as temperature, humidity, and solar radiation, splitting the data into the year’s four seasons and performing eight experiments according to the two algorithms and each season. The LSTM-RNN model produces the best results for the metrics in summer, achieving an R² = 0.9994, RMSE = 0.2698, MAE = 0.1449, and MAPE = 0.0041, meeting the acceptability criterion of ±2 °C hysteresis.

Agricultural greenhouses must accurately predict environmental factors to ensure optimal crop growth and energy management efficiency. However, the existing predictors have limitations when dealing with dynamic, non-linear, and massive temporal data. This study proposes four supervised learning techniques focused on linear regression (LR) and Support Vector Regression (SVR) to predict the internal temperature of a greenhouse. A meteorological station is installed in the greenhouse to collect internal data (temperature, humidity, and dew point) and external data (temperature, humidity, and solar radiation). The data comprises a one year, and is divided into seasons for better analysis and modeling of the internal temperature. The study involves sixteen experiments corresponding to the four models and the four seasons and evaluating the models’ performance using R${}^2$, RMSE, MAE, and MAPE metrics, considering an acceptability interval of ±2 ${}^{°}$C. The results show that LR models had difficulty maintaining the acceptability interval, while the SVR models adapted to temperature outliers, presenting the highest forecast accuracy among the proposed algorithms.

Remote laboratories are essential in addressing access and quality challenges in technical education. They enable students from various locations to engage with real equipment, overcome geographic and economic constraints, and provide solutions during crises, such as pandemics, when in-person learning is limited. As a key element of Education 4.0, remote labs promote technical skill development, enhance engineering education, and support diverse learning approaches. This study presents a remote laboratory based on Field Programmable Gate Arrays (FPGAs), developed using a waterfall methodology integrating IoT and Cloud Computing technologies to facilitate close interaction between hardware and software. The lab focuses on controlling DC, servo, and stepper motors, allowing students to apply theoretical concepts such as digital signals, pulse-width modulation (PWM), and data representation in bits in a practical setting. The testing phase involved 50 robotics and mechatronics engineering students who participated in hands-on sessions for one month, followed by a structured survey evaluating their experience, interaction, and the educational relevance of the platform. The survey shows high student satisfaction, highlighting the platform’s strengths and identifying areas for improvement. The results also underscore the system’s potential to significantly enhance the educational experience in remote environments, aligning with the United Nations Sustainable Development Goals (SDGs).

The accurate measurement of solar radiation is essential for applications in agriculture, renewable energy, and environmental monitoring. Traditional pyranometers provide high-precision readings but are often costly and inaccessible for large-scale deployment. This study explores the feasibility of using low-cost ambient light sensors combined with statistical and machine learning models based on linear, random forest, and support vector regressions to estimate solar irradiance. To achieve this, an Internet of Things-based system was developed, integrating the light sensors with cloud storage and processing capabilities. A dedicated solar radiation sensor (Davis 6450) served as a reference, and results were validated against meteorological API data. Experimental validation demonstrated a strong correlation between sensor-measured illuminance and solar irradiance using the random forest model, achieving a coefficient of determination (R²) of 0.9922, a root mean squared error (RMSE) of 44.46 W/m², and a mean absolute error (MAE) of 27.12 W/m². These results suggest that low-cost light sensors, when combined with data-driven models, offer a viable and scalable solution for solar radiation monitoring, particularly in resource-limited regions.

As the development of systems in smart homes is increasing, it is of ever-increasing importance to have data, which artificial intelligence methods and techniques can apply to recognize activities and patterns or to detect anomalies, with the aim of reducing energy consumption in the main home domestic services, and to offer users an alternative in the management of these resources. This paper describes the design and implementation of a platform based on the internet of things and a cloud environment that allows the user to remotely control and monitor Wi-Fi wireless e-switch in a home through a mobile application. This platform is intended to represent the first step in transforming a home into a smart home, and it allows the collection and storage of the e-switch information, which can be used for further processing and analysis.

The remote monitoring of vital signs and healthcare provision has become an urgent necessity due to the impact of the COVID-19 pandemic on the world. Blood oxygen level, heart rate, and body temperature data are crucial for managing the disease and ensuring timely medical care. This study proposes a low-cost wearable device employing non-contact sensors to monitor, process, and visualize critical variables, focusing on body temperature measurement as a key health indicator. The wearable device developed offers a non-invasive and continuous method to gather wrist and forehead temperature data. However, since there is a discrepancy between wrist and actual forehead temperature, this study incorporates statistical methods and machine learning to estimate the core forehead temperature from the wrist. This research collects 2130 samples from 30 volunteers, and both the statistical least squares method and machine learning via linear regression are applied to analyze these data. It is observed that all models achieve a significant fit, but the third-degree polynomial model stands out in both approaches. It achieves an R² value of 0.9769 in the statistical analysis and 0.9791 in machine learning.