Population growth and climate change lead agricultural cultures to face environmental degradation and rising of resistant diseases and pests. These conditions result in reduced product quality and increasing risk of harmful toxicity to human health. Thus, the prediction of the occurrence of diseases and pests and the consequent avoidance of the erroneous use of phytosanitary products will contribute to improving food quality and safety and environmental land protection. This study presents the design and construction of a low-cost IoT sensor mesh that enables the remote measurement of parameters of large-scale orchards. The developed remote monitoring system transmits all monitored data to a central node via LoRaWAN technology. To make the system nodes fully autonomous, the individual nodes were designed to be solar-powered and to require low energy consumption. To improve the user experience, a web interface and a mobile application were developed, which allow the monitored information to be viewed in real-time. Several experimental tests were performed in an olive orchard under different environmental conditions. The results indicate an adequate precision and reliability of the system and show that the system is fully adequate to be placed in remote orchards located at a considerable distance from networks, being able to provide real-time parameters monitoring of both tree and the surrounding environment.

Food waste results in an increased need for production to compensate for losses. Increased production is directly related to an increase in the environmental impact of agriculture and in the energy needs associated with it. To reduce food waste, the supply chain should maintain ideal preservation conditions. In horticultural products, temperature, and relative humidity are two of the main parameters to be controlled. Monitoring these parameters can help decision-making in logistics and routes management, as well as to diagnose and timely prevent food losses. In the present work, eighteen wireless traceability devices with temperature and relative humidity sensors monitored crates with horticultural products along a short-range distribution route with five stops (4 h 30 m). Sensor data and a location tag were sent via GSM for real-time monitoring. The results showed fluctuations in temperature and relative humidity that reached up to 7.4 °C and 35.3%, respectively. These fluctuations happened mostly due to frequent door opening, operational procedures, and irregular refrigeration conditions. Furthermore, the results brought attention to a procedure that creates unnecessary temperature fluctuations and energy losses. This study highlights the importance of individual monitorization of goods, for quality control and optimization of energy efficiency along the supply chain.

The food industry increasingly depends on technological assets to improve the efficiency and accuracy of fruit processing and quality control. This article enhances the application of computer vision with collaborative robotics to create a non-destructive system. The system can automate the detection and handling of fruits, particularly tomatoes, reducing the reliance on manual labor and minimizing damage during processing. This system was developed with a Raspberry Pi 5 to capture images of the fruit using a PiCamera module 3. After detecting the object, a command is sent to a Universal Robotics UR3e robotic arm via Ethernet cable, using Python code that integrates company functions and functions developed specifically for this application. Four object detection models were developed using the TensorFlow Object Detection API, converted to TensorFlow Lite, to detect two types of fruit (tomatoes) using deep learning techniques. Each fruit had two versions of the models. The models obtained 67.54% mAP for four classes and 64.66% mAP for two classes, A rectangular work area was created for the robotic arm and computer vision to work together. After 640 manipulation tests, a reliable area of 262 × 250 mm was determined for operating the system. In fruit sorting facilities, this system can be employed to automatically classify fruits based on size, ripeness, and quality. This ensures consistent product standards and reduces waste by sorting fruits according to pre-defined criteria. The system’s ability to detect multiple fruit types with high accuracy enables it to integrate into existing workflows, thereby increasing productivity and profitability for food processing companies. Additionally, the non-destructive nature of this technology allows for the inspection of fruits without causing any damage, ensuring that only the highest-quality produce is selected for further processing. This application can enhance the speed and precision of quality control processes, leading to improved product quality and customer satisfaction.

This study investigates the mechanical properties of thermoplastic polyurethane (TPU) 60A, which is a flexible material that can be used to produce soft robotic grippers using additive manufacturing. Tensile tests were conducted under ISO 37 and ISO 527 standards to assess the effects of different printing orientations (0°, 45°, −45°, 90°, and quasi-isotropic) and test speeds (2 mm/min, 20 mm/min, and 200 mm/min) on the material’s performance. While the printing orientations at 0° and quasi-isotropic provided similar performance, the quasi-isotropic orientation demonstrated the most balanced mechanical behavior, establishing it as the optimal choice for robust and predictable performance, particularly for computational simulations. TPU 60A’s flexibility further emphasizes its suitability for handling delicate objects in industrial and agricultural applications, where damage prevention is critical. Computational simulations using the finite element method were conducted. To verify the accuracy of the models, a comparison was made between the average stresses of the tensile test and the computational predictions. The relative errors of force and displacement are lower than 5%. So, the constitutive model can accurately represent the material’s mechanical behavior, making it suitable for computational simulations with this material. The analysis of strain rates provided valuable insights into optimizing production processes for enhanced mechanical strength. The study highlights the importance of tailored printing parameters to achieve mechanical uniformity, suggesting improvements such as biaxial testing and G-code optimization for variable thickness deposition. Overall, the research study offers comprehensive guidelines for future design and manufacturing techniques in soft robotics.

This study presents an innovative pedagogical approach aimed at enhancing the teaching of robotics within the broader context of STEM (science, technology, engineering, and mathematics) education across diverse academic levels. The integration of mobile robotics kits into a dynamic STEM-focused curriculum offers students an immersive and hands-on learning experience, fostering programming skills, advanced problem-solving, critical thinking, and spatial awareness. The motivation behind this research lies in improving the effectiveness of robotics education by addressing existing gaps in current strategies. It aims to better prepare students for this rapidly evolving field’s dynamic challenges and opportunities. To achieve this, detailed protocols were formulated that not only facilitate student learning but also cater to teacher training and involvement. These protocols encompass code documentation and examples, providing tangible representations of the practical outcomes of the course. In addition to the presented curriculum, this paper introduces the developed methodology that strategically leverages 3D-printing technology. The primary focus of this approach is to create captivating add-ons and establish a versatile workspace, actively promoting heightened engagement and facilitating the acquisition of knowledge among students. The research involves the development of tailored laboratory protocols suited to various academic levels, employing a systematic methodology aimed at deepening students’ comprehension of STEM concepts. Furthermore, an adaptable infrastructure for laboratory protocols and in-class testing was developed. The efficacy of this teaching/learning methodology is evaluated through student surveys, ensuring its continuous improvement. These protocols are to be integrated into both the robotics courses and teacher-training initiatives. This study aims to contribute to the field by using a dynamic STEM-driven approach based on mobile robotics. It outlines a strategic vision for better-preparing students and educators in the ever-evolving landscape of robotics education demanded by Industry 4.0 technologies.