Automated systems, regulated by algorithmic protocols and predefined set-points for feedback control, require the oversight and fine tuning of skilled technicians. This necessity is particularly pronounced in automated greenhouses, where optimal environmental conditions depend on the specialized knowledge of dedicated technicians, emphasizing the need for expert involvement during installation and maintenance. To address these challenges, this study proposes the integration of data acquisition technologies using Internet of Things (IoT) protocols and optimization services via reinforcement learning (RL) methodologies. The proposed model was tested in an industrial production greenhouse for the cultivation of industrial hemp, applying adapted strategies to the crop, and was guided by an agronomic technician knowledgeable about the plant. The expertise of this technician was crucial in transferring the RL model to a real-world automated greenhouse equipped with IoT technology. The study concludes that the integration of IoT and RL technologies is effective, validating the model’s ability to manage and optimize greenhouse operations efficiently and adapt to different types of crops. Moreover, this integration not only enhances operational efficiency but also reduces the need for constant human intervention, thereby minimizing labor costs and increasing scalability for larger agricultural enterprises. Furthermore, the RL-based control has demonstrated its ability to maintain selected temperatures and achieve energy savings compared to classical control methods.

Automated systems, regulated by algorithmic protocols and predefined set-points for feedback control, require the oversight and fine-tuning of skilled technicians. This necessity is particularly pronounced in automated greenhouses, where optimal environmental conditions depend on the specialized knowledge of dedicated technicians, emphasizing the need for expert involvement during installation and maintenance. To address these challenges, this study proposes the integration of data acquisition technologies using Internet of Things (IoT) protocols and optimization services via Reinforcement Learning (RL) methodologies. The proposed model was tested in an industrial production greenhouse for the cultivation of industrial hemp, applying adapted strategies to the crop, and was guided by an agronomic technician knowledgeable about the plant. The expertise of this technician was crucial in transferring the RL model to a real-world automated greenhouse equipped with IoT technology. The study concludes that the integration of IoT and RL technologies is effective, validating the model's ability to manage and optimize greenhouse operations efficiently and adapt to different types of crops. Moreover, this integration not only enhances operational efficiency but also reduces the need for constant human intervention, thereby minimizing labor costs and increasing scalability for larger agricultural enterprises. Furthermore, the RL-based control has demonstrated its ability to maintain selected temperatures and achieve energy savings compared to classical control methods.

Obesity is one of the most serious public health challenges of the 21st century and it is a threat to the life of people according to World Health Organization. In this scenario, family environment is important to establish healthy habits which help to reduce levels of obesity and control overweight in children. However, little efforts have been focused on helping parents to promote and have healthy lifestyles. In this paper, we present two smart device-based notification prototypes to promote healthy behavior with the aim of avoiding childhood overweight and obesity. The first prototype helps parents to follow a healthy snack routine, based on a nutritionist suggestion. Using a fridge magnet, parents receive graphical reminders of which snacks they and their children should consume. The second prototype provides a graphical reminder that prevents parents from forgetting the required equipment to practice sports. Prototypes were evaluated by nine nutritionists from three countries (Costa Rica, Mexico and Spain). Evaluations were based on anticipation of use and the ergonomics of human–system interaction according to the ISO 9241-210. Results show that the system is considered useful. Even though they might not be willing to use the system, they would recommend it to their patients. Based on the ISO 9241-210 the best ranked features were the system’s comprehensibility, the perceived effectiveness and clarity. The worst ranked features were the system’s suitability for learning and its discriminability.