Fernando Aragon-Rodriguez

The present study explores how Artificial Intelligence (AI) technologies can be effectively embedded in agroforestry engineering education. It highlights the growing importance of digital competences and AI in agricultural, forestry, and environmental sectors. The paper stresses that curricula must evolve to include tools like GIS, remote sensing, and machine learning, while also addressing faculty training, infrastructure needs, and licensing costs. University–industry collaboration is presented as crucial for aligning academic content with real-world demands. Ethical considerations—such as data privacy, algorithmic bias, and the role of human oversight—are emphasized, referencing UNESCO's guidelines. In conclusion, the study calls for comprehensive curricular reform, investment in infrastructure and educator training, and ethical safeguards to ensure graduates are well-prepared for a rapidly evolving, AI-driven job market.

Monitoring the development of greenhouse crops is essential for optimising yield and ensuring the efficient use of resources. A system for monitoring hemp (Cannabis sativa L.) cultivation under greenhouse conditions using computer vision has been developed. This system is based on open-source automation software installed on a single-board computer. It integrates various temperature and humidity sensors and surveillance cameras, automating image capture. Hemp seeds of the Tiborszallasi variety were sown. After germination, plants were transplanted into pots. Five specimens were selected for growth monitoring by image analysis. A surveillance camera was placed in front of each plant. Different approaches were applied to analyse growth during the early stages: two traditional computer vision techniques and a deep learning algorithm. An average growth rate of 2.9 cm/day was determined, corresponding to 1.43 mm/°C day. A mean MAE value of 1.36 cm was obtained, and the results of the three approaches were very similar. After the first growth stage, the plants were subjected to water stress. An algorithm successfully identified healthy and stressed plants and also detected different stress levels, with an accuracy of 97%. These results demonstrate the system’s potential to provide objective and quantitative information on plant growth and physiological status.

Traditional and intensive olive groves account for a large part of today’s olive orchards and their harvesting is based on trunk shakers. The vibration parameters set in these machines and the biomechanical properties of the olive tree influence the detachment process. Tree geometry and morphology are fundamental factors influencing the propagation of vibration. Understanding the effect of tree geometry on vibration propagation can provide useful indications for tree training and pruning. The aim of this work is to study the effect of branch inclination on the vibration response when a trunk shaker is applied, as there is no experimental information on this variable in the literature. We randomly selected 80 olive trees from an intensive olive orchard, and the acceleration of the trunk and one of the main branches was recorded for each tree when forced vibration was applied using a trunk shaker. Two triaxial MEMS accelerometers were used to measure the vibration and, in addition, the location of each sensor, the trunk and branch diameters and the branch angle were measured. It was observed that in all cases there was an amplification of acceleration from the trunk to the branch: the mean acceleration transmissibility value was 139.5%. The highest acceleration values occurred in branches with an inclination between 30 and 60 degrees, which also had the highest acceleration transmissibility, with an increase of 13.8-16.8% and 6.3-10.5%, respectively. In addition, the highest relative kinetic energy ratio was higher in branches with an inclination between 30 and 60 degrees.

The olive tree is an important crop in Portugal, where different levels of intensification coexist. The traditional olive orchards present profitability problems, mainly due to harvesting, so there has been a drastic reconversion towards high-density or super-high-density olive orchards. The latter present major constraints due to very specific needs for their use, being practically destined for new orchards. Consequently, the possibility of using systems based on canopy shakers in high-density olive orchards with local varieties is promising. The objective of this work is to evaluate a prototype canopy shaker for the harvesting of high-density olive orchards of the Portuguese variety ‘Cobrançosa’. The evaluation is based on the study of canopy shaking in order to adapt canopy training and the adaptability of the machine. For this purpose, the vibration of 72 points of the tree canopy was recorded and a qualitative assessment of the harvest was carried out. Differences were found between the different zones according to the direction of the forward movement of the harvester and the distance to the trunk. These differences were associated with the values obtained for fruit detachment, and a greater quantity of fruit was harvested in the areas of the canopy in contact with the rods.

El Aprendizaje Basado en Retos (ABR) es una metodología que fomenta la creatividad y el pensamiento crítico mediante el planteamiento de retos relevantes para la formación del estudiante claramente identificables como situaciones reales. En la agricultura actual se están empleando nuevas técnicas y tecnologías las cuales nos permiten conocer y gestionar la variabilidad de la producción para mejorar la sostenibilidad y eficiencia de los sistemas agrícolas, conformándose la denominada agricultura de precisión (AP). Se plantea que los alumnos de 2º curso del Máster de Ingeniería Agronómica, a través del ABR y los principios de la AP, realicen una experiencia real del manejo de un cultivo. Para ello, se ha orientado el aprendizaje hacia la toma de decisiones en la Gestión de Flotas de vehículos agrícolas y la distribución variable de insumos, articulándose en cuatro actividades guía. Participaron un total de 26 alumnos, trabajándose habilidades blandas, como la comunicación, toma de decisiones y orientación a resultados, y habilidades técnicas relacionadas con el dominio agrícola y herramientas tecnológicas específicas. Se evaluaron los aspectos citados mediante un cuestionario teórico, evaluación de las actividades guía y una exposición de la solución aportada, alcanzando puntuaciones más elevadas que en cursos anteriores con metodologías convencionales.

The introduction of a mechanical harvesting process for oranges can contribute to enhancing farm profitability and reducing labour dependency. The objective of this work is to determine the spread of the vibration in citrus tree canopies to establish recommendations to reach high values of fruit detachment efficiency and eliminate the need for subsequent hand-harvesting processes. Field tests were carried out with a lateral tractor-drawn canopy shaker on four commercial plots of sweet oranges. Canopy vibration during the harvesting process was measured with a set of triaxial accelerometer sensors with a datalogger placed on 90 bearing branches. Monitoring of the vibration process, fruit production, and branch properties were analysed. The improvement of fruit detachment efficiency was possible if both the hedge tree and the machinery were mutually adjusted. The hedge should be trained to facilitate access of the rods and to encourage external fructification since the internal canopy branches showed 43% of the acceleration vibration level of the external branches. The machine should be adjusted to vibrate the branches at a vibration time of at least 5.8 s, after the interaction of the rod with the branch, together with a root mean square acceleration value of 23.9 m/s² to a complete process of fruit detachment.