World agriculture is facing a great challenge since it is necessary to find a sustainable way to increase food production. Current trends in advancing the agriculture sector are based on leveraging remote sensing technology and the use of biostimulants. However, the efficient implementation of both of these on a commercial scale for the purposes of productivity improvement remains a challenge. Thus, by proposing a crop monitoring strategy based on remote sensing data, this paper aims to verify and anticipate the impact of applying a Glycinebetaine biostimulant (GB) on the final yield. The study was carried out in a rice-producing area in Eastern Spain (Valencia) in 2021. GB was applied by drone 33 days after sowing (tillering phase). Phenology was monitored and crop production parameters were determined. Regarding satellite data, Sentinel-2 cloud-free images were obtained from sowing to harvest, using the bands at 10 m. Planet data were used to evaluate the results from Sentinel-2. The results show that GB applied 33 days after sowing improves both crop productive parameters and commercial yield (13.06% increase). The design of the proposed monitoring strategy was based on the dynamics and correlations between the visible (green and red) and NIR bands. The analysis showed differences when comparing the GB and control areas, and permitted the determination of the moment in which the effect of GB on yield (tillering and maturity) may be greater. In addition, an index was constructed to verify the crop monitoring strategy, its mathematical expression being: NCMI = (NIR − (red + green))/(NIR + red + green). Compared with the other VIs (NDVI, GNDVI and EVI2), the NCMI presents a greater sensitivity to changes in the green, red and NIR bands, a lower saturation phenomenon than NDVI and a better monitoring of rice phenology and management than GNDVI and EVI2. These results were evaluated with Planet images, obtaining similar results. In conclusion, in this study, we confirm the improvement in rice crop productivity by improving sustainable plant nutrition with the use of biostimulants and by increasing the components that define crop yield (productive tillers, spikelets and grains). Additionally, crop monitoring using remote sensing technology permits the anticipation and understanding of the productive behavior and the evolution of the phenological stages of the crop, in accordance with crop management.

Climate change worsens abiotic stresses, primarily due to high temperatures, which have a negative impact on avocado productivity, leading to reduced crop yields, affecting fruit set and abscission. To tackle these challenges, antioxidants such as glycine, choline, and proline can enhance plant tolerance to these stressors and minimize plant cell damage. This work aimed to use these antioxidants to improve avocado commercial yield and quality under challenging environmental conditions. This study was conducted at the experimental farm of the Polytechnic University of Valencia, Spain, to evaluate the effects of glycine, choline, and proline on ‘Hass’ Persea americana plants. The research took place during the 2022–2023 and 2023–2024 seasons in a 2.0 ha orchard, using a randomized design with two treatments: one with antioxidants and the other without. Substances were applied at specific phenological phases, as the BBCH code indicated. Tree growth parameters, including trunk diameter, height, crown diameter, and tree canopy volume, were measured using geometric formulas. Leaf samples were collected to analyze the nutrient concentrations of N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn using atomic emission spectrometry. Marketable fruit yield and quality parameters such as fat, fiber, and protein content were evaluated using the Association of Official Agricultural Chemists (AOAC) methods. The results showed that antioxidants did not significantly affect tree growth but altered leaf mineral nutrient composition. N and P concentrations were reduced, while K and Ca concentrations were increased. Mn and Zn levels were higher in the treated plants, whereas Cu levels were higher in the control plants. Productivity significantly improved, with a 49% increase in fruit yield, larger fruit size, and a 7% increase in fat content, though fiber and protein remained unchanged. These results show the selective benefits of antioxidants in optimizing avocado yield and quality under stress.

Precision agriculture can determine the amount of nitrogen (N) required in each area to optimize yield and nitrogen use efficiency (NUE). The use of variable rate technology (VRT) for planning N fertilization has often relied on techniques that are unfeasible for farmers with limited resources. This study aims to present a variable fertilization plan for wheat and barley, along with a protocol to determine the optimal timing for the second nitrogen (N) application, thereby minimizing the need for in situ crop monitoring. Two approaches are studied: a more straightforward sensor-based method and a map-based method. The sensor-based approach involved modeling the maximum NDVI based on the observed value at the time of application and the required N level, achieving an R² of 0.55 ± 0.06 and 0.72 ± 0.04, an MAE of 0.025 ± 0.002 and 0.039 ± 0.002, and an RMSE of 0.049 ± 0.007 and 0.055 ± 0.004 for wheat and barley, respectively. The map-based approach relied on training models to estimate the nitrogen dose to be applied based on the target yield and reflectance data from Sentinel-2 at the time of application. Using random forest algorithms, an R² of 0.97 ± 0.01 and 0.96 ± 0.02, an MAE of 3.33 ± 0.20 kg N ha⁻¹ and 2.01 ± 0.13 kg N ha⁻¹, and an RMSE of 4.79 ± 0.31 kg N ha⁻¹ and 3.27 ± 0.58 kg N ha⁻¹ for wheat and barley, respectively.

Modern combine harvesters can collect real-time geolocated yield data, but it is subject to errors. Various protocols have been proposed to clean this data, each with varying levels of complexity. This data is valuable for precision agriculture to implement site-specific management and to train models to predict yield using remote sensing data. Machine learning and deep learning techniques have shown their potential for precision agriculture, and their performance shows no significant differences between models trained with data cleaned using a computationally demanding protocol or a simpler one, such as parametric filtering. However, parametric filtering approaches primarily rely on statistics that are highly sensitive to data distribution and do not effectively filter inliers. The objective of this study is to develop a data-cleansing method that leverages robust statistical measures, specifically the median and interquartile range, to effectively identify and filter outliers and inliers while retaining valid observations in datasets collected from combine harvesters, thereby minimizing the influence of non-normal data distributions. Different levels of data cleaning were applied to a total of 7399 ha of wheat and barley crops, and the quality of each cleaning level was compared. The selected protocol improved the spatial structure of the data, deleting up to 42% and 33% of the data at the polygon level, for wheat and barley, respectively. It increased the mean and median, and decreased the standard deviation and coefficient of variation of the data. Between 78.7% and 82.9% of the fields showed a normal distribution after applying the selected method, and machine learning performance improved compared with the raw data. Compared with previous data cleaning studies, the present work proposes an automatic, low-computational, parametric filtering method that uses robust statistics for non-normal distributions. In addition, its scalability has been demonstrated by applying the method to a large dataset, improving data quality and the performance of yield-prediction ML models in all cases.

High-resolution yield forecasting is essential for advancing precision agriculture and improving the sustainability of wheat and barley production. While most previous studies focus on field-scale predictions, pixel-level approaches are needed to capture intra-field variability and support site-specific management. This paper evaluates the performance of machine learning models for 10 m resolution yield prediction using multi-temporal Sentinel-2 surface reflectance data across seven major cereal-producing regions in Spain. Yield monitor data from winter wheat and barley fields collected over five growing seasons (2020–2024) were combined with spectral bands and vegetation indices. Random Forest (RF) and XGBoost (XGB) models were trained at five phenological stages expressed as days before harvest (DBH) and validated using both internal (2020–2023) and independent external (2024) datasets. Model accuracy increased as harvest approached. In external validation, RF achieved the best performance for wheat (R² = 0.77; RMSE ≈ 697 kg · ha⁻¹), while XGB performed best for barley (R² = 0.86; RMSE ≈ 744 kg · ha⁻¹). Visible, red-edge, and SWIR bands were the most informative predictors, especially during grain filling and senescence. Results demonstrate the potential of multi-temporal Sentinel-2 data and machine learning for accurate, transferable, pixel-level yield forecasting in Mediterranean cereal systems.

Esta investigación evalúa cómo la educación superior inculca el Objetivo de Desarrollo Sostenible 13 (Acción por el Clima) y su efecto en la conciencia y conocimientos de los estudiantes de ingeniería. La urgencia del cambio climático impone la educación como herramienta clave para impulsar el compromiso con los ODS. Mediante encuestas realizadas antes y después de sesiones de concienciación, se identificó una carencia inicial de conocimientos sobre los ODS, con un 60% de los estudiantes inicialmente ajenos a estos. Sin embargo, se registró un aumento significativo en la comprensión post-intervención, reflejando la influencia de la disciplina en la receptividad a la sostenibilidad. El estudio también reveló variaciones de percepción según género, edad y origen educativo; las mujeres y estudiantes mayores tenían una consciencia más aguda del clima, y los provenientes de la educación pública, mayor conocimiento de los ODS comparado con los de entornos privados. Estos datos apuntan a la necesidad de métodos de enseñanza adaptativos y de integrar los ODS de forma más efectiva en los currículos y la investigación, preparando así a los futuros profesionales para enfrentar los desafíos de la sostenibilidad global.