Rice cultivation in the Guadalquivir River marshes of southern Spain is increasingly constrained by irrigation water salinity, exacerbated by drought and seawater intrusion. This study assessed the agronomic, physiological, and spectral responses of indica and japonica cultivars under commercial farming conditions across a natural salinity gradient (mean electrical conductivity of irrigation water ranging from 3.1 to 6.9 dS m(-)(1)). Field measurements included yield, growth traits, and leaf ion concentrations, complemented with Sentinel-2 vegetation indices and integrated using Generalized Additive Models (GAMs). Rice yield declined steeply with salinity, with up to 70 % losses between 3 and 7 dS m(-)(1) . Rice grown in medium-salinity fields maintained Na/K ratios comparable to low-salinity fields, suggesting that compensatory K+ uptake mitigated yield penalties. By contrast, high salinity led to marked ionic imbalance, particularly in japonica cultivars, which consistently exhibited higher Na/K ratios than indica. Spectral data revealed that broad-band greenness indices (NDVI, GNDVI, EVI, SAVI, NDRE) effectively captured early osmotic effects (<60 DAS), while MCARI uniquely detected late-stage ionic stress during reproductive phases. GAM analysis confirmed two phenological windows of higher sensitivity to salinity-vegetative establishment and reproductive development-while demonstrating the predictive utility of combined physiological and spectral indicators (LOOCV R-2 = 0.867). These findings underscore the need for growth phase-specific management and the potential of integrating physiological and remote sensing data to support adaptation strategies in Mediterranean rice systems.

This study develops a cloud-based remote sensing framework in Google Earth Engine to assess the evolution of evapotranspiration and irrigation performance across 49 representative irrigation districts in Andalusia (southern Spain), covering more than 212,000 ha during the period 2004–2019. The approach combines optical and thermal satellite data to estimate unstressed crop evapotranspiration (ETus) using vegetation-index-based crop coefficients and actual evapotranspiration (ETa) using the SSEBop energy balance model. These variables were integrated with irrigation adequacy indicators, including the Crop Water Stress Index (CWSI) and Relative Irrigation Supply (RIS), to evaluate long-term irrigation dynamics. Results reveal a clear divergence between ETus and ETa trends. ETus increased significantly across all irrigation typologies, driven by three concurrent processes: the progressive replacement of low-water-demand herbaceous crops, such as winter cereals, with woody perennials with expanding canopies; a shift within the remaining herbaceous fraction toward species with higher potential water requirements; and improved irrigation uniformity following the conversion of surface irrigation to pressurized systems. In contrast, ETa declined in inland irrigation systems and remained broadly stable in subtropical districts. This divergence indicates a sustained intensification of managed water stress, consistent with the widespread adoption of deficit irrigation strategies driven by rising irrigation costs, modernization of delivery systems, and regulatory constraints on water allocations. Regional analyses show no evidence of a rebound effect following modernization, as total actual crop water consumption slightly declined despite the increase in potential crop water demand.

Precision agriculture depends on high-resolution soil and crop measurements across temporal and spatial scales. This work presents a novel soil moisture measurement system integrated into a phenotyping platform, enhancing crop trait assessment. The system, featuring an onboard soil sensor and linear actuator, offers customizable spatial coverage, georeferenced data acquisition, operational versatility, and seamless integration with other crop sensors. Tested in wheat under two water treatments, the system detected soil moisture differences linked to physiological traits like the stomatal conductance to vapor pressure deficit ratio (Gs/VPD). These measurements enhance understanding of the soil-plant-atmosphere system, essential for precision agriculture implementation. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Understanding the soil-plant-atmosphere continuum is essential for advancing precision agriculture and improving plant-water relations. This study integrates real-time sensor data, remote sensing, and statistical modelling to monitor key physiological and environmental variables such as net photosynthesis (A), stomatal conductance (Gs), vapor pressure deficit, and soil moisture. A multi-sensor phenotyping platform with a novel soil moisture system enabled high-resolution monitoring. In wheat trials, hyperspectral data predicted A and Gs with high accuracy (R-squared of 0.74 and 0.71). This approach reveals seasonal dynamics of crop traits and water status, supporting scalable, data-driven solutions for better crop management.

Strawberry (Fragraria x ananassa) is a crop affected by various soil-borne fungal pathogens with mostly non-specific foliar symptoms and often requiring laboratory isolation for correct diagnosis. Moreover, these nonspecific foliar symptoms, appreciated by the human eye, appear after some time following infection by the pathogen. Early detection of plant diseases is one of the primary objectives in agriculture because it may contribute to identifying more tolerant cultivars in breeding programs and optimise pesticide use in agricultural production with earlier applications in emerging disease foci. New technologies, such as remote sensing and machine learning (ML) algorithms, have arisen as potential tools to improve the ability to detect and classify different crop diseases. The combined use of hyperspectral imagery and ML algorithms were investigated to detect and classify the physiological stress caused by early infections of Fusarium wilt in strawberry plants. Six ML models, namely artificial neural network, decision tree, K-nearest neighbour, support vector machine, multinomial logistic regression and Naïve Bayes were developed to estimate physiological stress associated with Fusarium wilt disease. The results showed that stomatal conductance (gs) and photosynthesis (A) declined even without visual symptoms of the disease. Among the six ML models evaluated, the artificial neural network model showed the highest classification performance with an overall accuracy of 81%, regardless of the physiological parameter utilized for model training. Moreover, the artificial neural network accurately predicted the absolute values of both physiological parameters (gs and A) based on the complete spectral signature from visually healthy foliar tissue, achieving coefficients of determination of 84% and 81%, respectively. Consequently, ML models utilizing physiological response data and hyperspectral imaging exhibited remarkable robustness, facilitating the estimation of Fusarium wilt severity in strawberry plants even without visual symptoms.

This study evaluates the efficacy of hyperspectral data for detecting yellow and brown rust in wheat, employing machine learning models and the SMOTE (Synthetic Minority Oversampling Technique) augmentation technique to tackle unbalanced datasets. Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Naïve Bayes (GNB) models were assessed. Overall, SVM and RF models showed higher accuracies, particularly when utilizing SMOTE-enhanced datasets. The RF model achieved 70% accuracy in detecting yellow rust without data alteration. Conversely, for brown rust, the SVM model outperformed others, reaching 63% accuracy with SMOTE applied to the training set. This study highlights the potential of spectral data and machine learning (ML) techniques in plant disease detection. It emphasizes the need for further research in data processing methodologies, particularly in exploring the impact of techniques like SMOTE on model performance.

The water footprint is an increasingly demanded environmental sustainability indicator for certifications and labels in agricultural production. Processing tomatoes are highly water-intensive, and existing studies on water footprint have uncertainties and do not consider the impact of different irrigation configurations (e.g., surface drip irrigation (SDI) and subsurface drip irrigation (SSDI)) and irrigation strategies. This study presents a two-year experimental investigation to determine the water footprint of processing tomatoes grown in southern Spain (Andalusia) and the impact of SSDI and deficit irrigation. Five irrigation treatments were established: SDI1 (surface drip irrigation without water limitation), SDI2 (surface drip irrigation without water limitation adjusted by soil moisture readings), SSDI1 (subsurface drip irrigation without water limitation and a dripline depth of 15 cm), SSDI2 (similar to SSDI1 but with mild/moderate water deficit during the fruit ripening stage), and SSDI3 (subsurface drip irrigation without water limitation and a dripline depth of 35 cm (first year) and 25 cm (second year)). Measurements included crop vegetative growth, leaf water potential, leaf gas exchange, nitrate concentration in soil solution, and crop yield and quality. The soil water balance components (actual evaporation, actual transpiration, deep drainage), necessary for determining the total crop water footprint, were simulated on a daily scale using Hydrus 2D software. Results indicated that SSDI makes more efficient use of irrigation water than SDI. The water footprint of SSDI1 was 20–35% lower than that of SDI1. SSDI2 showed similar water footprint values to SDI1 under highly demanding environmental conditions and significantly lower values (≈40%) in a year with lower evaporative demand. The dripline depth in SSDI was critical to the water footprint. With a 35 cm installation depth, SSDI3 had a significantly higher water footprint than the other treatments, while the values were similar to SSDI1 when the depth was reduced to 25 cm.

This research delves into the nuanced dynamics influencing photocurrent generated in bifacial photovoltaic modules within the framework of agrovoltaic applications. Our findings underscore the necessity of using spectral data over absolute values regards to the wavelength dependence for precise energy yield predictions. Particularly, it is demonstrated that the ground reflectance plays a pivotal role. The type of soil (compact or tilled) and crop growth cycle contribute to temporal variations, significantly affecting energy production.In order to validate the proposed methodology for estimating photocurrent generated by APV systems based on spectral data, we conducted tests in two different irradiance conditions, deliberately chosen for their contrast: clear sky and hazy sky conditions. Experimental measurements were conducted for global, direct, and diffuse irradiance spectral components in both atmospheric scenarios. Additionally, we performed comprehensive spectral reflectance measurements for various soil types and crops throughout an entire growth cycle to depict the temporal variations in these values. It is observed an overestimation in the ratio between front and rear photocurrent generated by the bifacial PV module when the model relies on absolute values of solar irradiance and ground reflectance, compared to utilizing spectral input data. Furthermore, the analysis of absolute values fails to reveal a significant dependence on atmospheric conditions.In summary, this research discussed the implications of spectral data, geometry and atmospheric conditions, for bifacial PV modules in agrovoltaic applications.

Almond plantations are expanding worldwide, specifically in Spain; the new orchards are often designed under more intensive systems in comparison to the traditional rainfed orchards frequently found in the Mediterranean Sea basin. In these new areas, water is the main limiting factor, and therefore, the present research is aimed at quantitatively analyzing previous findings obtained in irrigation field trials carried out in Spain with mature almond trees. The goal was to derive applied water-production functions and compare sustained and regulated deficit irrigation strategies to provide robust information on the marginal water productivity and the preferred irrigation option to be applied under water scarcity conditions. This quantitative analysis reported a yield increase as water application increased, with the highest potential yield of about 2500 kg/ha achieved with around 1000 mm of irrigation water applied. Under severe water restrictions, similar responses were observed regardless of the deficit irrigation technique employed. In contrast, under moderate water stress, it seems more advantageous to apply a regulated deficit irrigation strategy rather than a sustained deficit strategy. The reported results are useful for deriving more sustainable irrigation protocols and highlight the need to optimize other inputs in addition to water to take full advantage of the irrigation intensification to be carried out in the new almond plantations.

Twenty years after the Bologna Declaration and a decade after Spanish university engineering degrees were updated to comply with the European Credit Transfer System (ECTS), there is still uncertainty on the degree of adaptation to the ECTS system of the final degree project (FDP) course in engineering programs, especially in terms of the workloads allocated to students. The inherent characteristics of the FDP course, with all the learning activities of an unstructured nature, make the real student workload as well as that of the FDP teachers very uncertain. This study addresses this issue by (1) identifying the nature of the unstructured student learning activities related to the FDP course, (2) measuring the time spent by students in the different FDP learning activities throughout the course, and (3) measuring the workload of FDP teachers. A user-friendly smartphone application was configured so that students and teachers in the agricultural engineering degree program at the University of Seville registered the time spent daily on each of the identified FDP learning (students) and supervising (instructors) activities. The results showed that the reported FDP workloads by students who passed the FDP course in either of the two exam periods of the academic year were not significantly higher than the nominal ECTS credit hours stipulated for the FDP course. The FDP teachers reported notably higher workloads than those stipulated by the university regulations. No significant correlation was found between student workload and FDP scores.

EDITORIAL article Front. Plant Sci., 22 November 2022Sec. Technical Advances in Plant Science Volume 13 - 2022 | https://doi.org/10.3389/fpls.2022.1079022

The leaf area index (LAI) is a biophysical crop parameter of great interest for agronomists and plant breeders. Direct methods for measuring LAI are normally destructive, while indirect methods are either costly or require long pre- and post-processing times. In this study, a novel deep learning-based (DL) model was developed using RGB nadir-view images taken from a high-throughput plant phenotyping platform for LAI estimation of maize. The study took place in a commercial maize breeding trial during two consecutive growing seasons. Ground-truth LAI values were obtained non-destructively using an allometric relationship that was derived to calculate the leaf area of individual leaves from their main leaf dimensions (length and maximum width). Three convolutional neural network (CNN)-based DL model approaches were proposed using RGB images as input. One of the models tested is a classification model trained with a set of RGB images tagged with previously measured LAI values (classes). The second model provides LAI estimates from CNN-based linear regression and the third one uses a combination of RGB images and numerical data as input of the CNN-based model (multi-input model). The results obtained from the three approaches were compared against ground-truth data and LAI estimations from a classic indirect method based on nadir-view image analysis and gap fraction theory. All DL approaches outperformed the classic indirect method. The multi-input_model showed the least error and explained the highest proportion of the observed LAI variance. This work represents a major advance for LAI estimation in maize breeding plots as compared to previous methods, in terms of processing time and equipment costs.

EDITORIAL article Front. Plant Sci., 18 November 2022Sec. Technical Advances in Plant Science https://doi.org/10.3389/fpls.2022.1079148

Wheat rust is a major wheat crop disease globally. Wheat production could benefit from the development of rapid, accurate and non-destructive identification methods for wheat rust. In this sense, the objective of this work was to develop a methodology to identify yellow rust severity on wheat plants using hyperspectral imaging and 3D point clouds analysis. The 3D reconstructed plants from high-resolution point clouds were used to derive models to estimate growth-related plant traits. Spectral features (i.e. selected vegetation indices) showed promising results to predict the level of severity of leaf rust disease in wheat plants. Although the results are promising, they must be considered as preliminary as data from only one growth cycle were used in the analyses.

In this study, a novel AI-based model, based on a convolutional neural network, has been developed and validated for leaf area index (LAI) estimations of maize using downward facing RGB images taken from a high-throughput plant phenotyping platform. Ground truth LAI values were obtained by measuring the leaf dimensions of selected plants and using an allometric relationship. The results obtained from the model approach were compared against ground truth values and LAI estimations from a classic indirect method based on hemispherical images and gap fraction theory. The model showed good performance with ground truth values and LAI estimations by the classic indirect method. This represents a major advance in LAI estimates in maize plots when compared to previous methods, particularly in terms of processing time and equipment cost.

Current land surface schemes in weather and climate models make use of the so-called coupled photosynthesis–stomatal conductance ( A–g s ) models of plant function to determine the surface fluxes that govern the terrestrial energy, water and carbon budgets. Plant physiology is controlled by many environmental factors, and a number of complex feedbacks are involved, but soil moisture control on root water uptake is primary, particularly in sub-tropical to temperate ecosystems. Land surface models represent plant water stress in different ways, but most implement a water stress factor, β , which ranges linearly (more recently also curvilinearly) between β = 1 for unstressed vegetation and β = 0 at the wilting point, expressed in terms of volumetric water content ( θ ). β is most commonly used to either limit A or g s , and hence carbon and water fluxes, and a pertinent research question is whether these treatments are in fact interchangeable. Following Egea et al. (Agricultural and Forest Meteorology, 2011, 151 (10), 1,370–1,384) and Verhoef et al. (Agricultural and Forest Meteorology, 2014, 191, 22–32), we have implemented new β treatments, reflecting higher levels of biophysical complexity in a state-of-the-art LSM, Joint UK Land Environment Simulator, by allowing root zone soil moisture to limit plant function non-linearly and via individual routes (carbon assimilation, stomatal conductance, or mesophyll conductance) as well as any (non-linear) combinations thereof. The treatment of β does matter to the prediction of water and carbon fluxes: this study demonstrates that it represents a key structural uncertainty in contemporary LSMs, in terms of predictions of gross primary productivity, energy fluxes and soil moisture evolution, both in terms of climate means and response to a number of European droughts, including the 2003 heat wave. Treatments allowing ß to act on vegetation fluxes via stomatal and mesophyll routes are able to simulate the spatiotemporal variability in water use efficiency with higher fidelity during the growing season; they also support a broader range of ecosystem responses, e.g., those observed in regions that are radiation limited or water limited. We conclude that current practice in weather and climate modelling is inconsistent, as well as too simplistic, failing to credibly simulate vegetation response to soil water stress across the typical range of variability that is encountered for current European weather and climate conditions, including extremes of land surface temperature and soil moisture drought. A generalized approach performs better in current climate conditions and promises to be, based on responses to recently observed extremes, more trustworthy for predicting the impacts of climate change.