Assessing plant water status accurately in both time and space is crucial for maintaining satisfactory crop yield and quality standards, especially in the face of a changing climate. Remote sensing technology offers a promising alternative to traditional in situ measurements for estimating stem water potential (Ψ_stem). In this study, we carried out field measurements of Ψ_stem in an irrigated olive orchard in southern Italy during the 2021 and 2022 seasons. Water status data were acquired at midday from 24 olive trees between June and October in both years. Reflectance data collected at the time of Ψ_stem measurements were utilized to calculate vegetation indices (VIs). Employing machine learning techniques, various prediction models were developed by considering VIs and spectral bands as predictors. Before the analyses, both datasets were randomly split into training and testing datasets. Our findings reveal that the random forest model outperformed other models, providing a more accurate prediction of olive water status (R² = 0.78). This is the first study in the literature integrating remote sensing and machine learning techniques for the prediction of olive water status in order to improve olive orchard irrigation management, offering a practical solution for estimating Ψ_stem avoiding time-consuming and resource-intensive fieldwork.

Olive agro-ecosystems in southern Italy have been heavily damaged due to Xylella fastidiosa subsp. pauca (Xfp). Replacing the Xfp-infected olive-growing areas with economically viable fruit tree species is thought to be a practical control measure. A land suitability analysis can provide an appropriate evaluation of a crop’s suitability in these areas. We evaluate the suitability of almond (Prunus dulcis B.), fig (Ficus carica L.), hazelnut (Corylus avellana L.), kiwifruit (Actinidia chinensis P.), pistachio (Pistacia vera L.), and pomegranate (Punica granatum L.) as fruit tree species immune/resistant to Xfp to be planted within the Xfp-infected olive-growing areas in the Apulia region to compensate for economic and environmental losses. Climate and soil data were used to carry out the land suitability analysis. We combined information for each parameter to obtain the overall suitability maps for the six proposed fruit tree crops using GIS (Geographic Information System). The analysis showed that the Xfp-infected olive-growing areas are suitable for the plantation of most of the proposed fruit tree crops, with different suitability levels as the climate and soil conditions vary among the study areas. In particular, large olive-growing areas are suitable for the cultivation of pomegranate (268,886 ha), fig (103,975 ha), and almond (70,537 ha), followed by kiwifruit (43,018 ha) and pistachio (40,583 ha). Hazelnut, with just 2744 ha of suitable land, was the species with fewer suitable areas in these semi-arid environments. This is the first study to provide practical containment measures against the diffusion of Xfp in southern Italy. Our results can help in the selection of the right immune/resistant tree species for replanting in Xfp-infected zones, therefore providing guidelines within the decision-making process to encourage the planting of some underrepresented fruit tree crops with viable economic values as well.

This study investigated the application of high-resolution satellite imagery from SuperDove satellites combined with machine learning algorithms to estimate the spatiotemporal variability of some winter wheat parameters, including the relative leaf chlorophyll content (RCC), relative water content (RWC), and aboveground dry matter (DM). The research was carried out within an experimental field in Southern Italy during the 2024 growing season. Different machine learning (ML) algorithms were trained and compared using spectral band data and calculated vegetation indices (VIs) as predictors. Model performance was assessed using R² and RMSE. The ML models tested were random forest (RF), support vector regressor (SVR), and extreme gradient boosting (XGB). RF outperformed the other ML algorithms in the prediction of RCC when using VIs as predictors (R² = 0.81) and in the prediction of the RWC and DM when using spectral bands data as predictors (R² = 0.71 and 0.87, respectively). Model explainability was assessed with the SHAP method. A SHAP analysis highlighted that GNDVI, Cl1, and NDRE were the most important VIs for predicting RCC, while yellow and red bands were the most important for DM prediction, and yellow and nir bands for RWC prediction. The best model found for each target was used to model its seasonal trend and produce a variability map. This approach highlights the potential of integrating ML and high-resolution satellite imagery for the remote monitoring of wheat, which can support sustainable farming practices.

Chlorophyll fluorescence is a useful indicator of a plant’s physiological status, particularly under stress conditions. Remote sensing is an increasingly adopted technology in modern agriculture, allowing the acquisition of crop information (e.g., chlorophyll fluorescence) without direct contact, reducing fieldwork. The objective of this study is to improve the monitoring of olive tree fluorescence (Fv′/Fm′) via remote sensing in a Mediterranean environment, where the frequency of stress factors, such as drought, is increasing. An advanced approach combining explainable artificial intelligence and multispectral Sentinel-2 satellite data was developed to predict olive tree fluorescence. Field measurements were conducted in southeastern Italy on two olive groves: one irrigated and the other one under rainfed conditions. Sentinel-2 reflectance bands and vegetation indices were used as predictors and different machine learning algorithms were tested and compared. Random Forest showed the highest predictive accuracy, particularly when Sentinel-2 reflectance bands were used as predictors. Using spectral bands preserves more information per observation, enabling models to detect variations that VIs might miss. Additionally, raw reflectance data minimizes potential bias that could arise from selecting specific indices. SHapley Additive exPlanations (SHAP) analysis was performed to explain the model. Random Forest showed the highest predictive accuracy, particularly when using Sentinel-2 reflectance bands as predictors. Key spectral regions associated with Fv′/Fm′, such as red-edge and NIR, were identified. The results highlight the potential of integrating remote sensing and machine learning to improve olive grove management, providing a useful tool for early stress detection and targeted interventions.

Grapevine is an essential socio-economic crop in the Mediterranean regions. However, its cultivation is threatened by climate change, particularly due to the increase in drought events. Traditional indicators of plant water status, i.e., predawn leaf water potential (Ψ _pd ), are helpful but labor-intensive. Furthermore, another index based on visual assessment of the shoot apex (i.e., iG-Apex), has been recently used to evaluate the water stress of grapevine plants. The application of remote sensing technologies and machine learning models gives the possibility to monitor grapevine spatio-temporal variability of plant physiological indicators in a non-destructive way. This study aims to develop an explainable artificial intelligence (XAI) framework to predict Ψ _pd and iG-Apex in two rainfed vineyards using high-resolution multispectral imagery from the Planet SuperDove constellation. Five machine learning models—extreme gradient boosting, random forest, support vector regressor, ElasticNet, and Linear Regression—were trained and compared. Extreme gradient boosting achieved the best performance for Ψ _pd prediction (R² = 0.778), while random forest outperformed for iG-Apex prediction (R² = 0.615). SHAP and ICE analyses were carried out for models’ explainability. The analyses revealed that visible and NIR bands were the most important predictors, influencing both Ψ _pd and iG-Apex prediction. Our results highlight the potential of combining XAI and remote sensing to support grapevine management under Mediterranean conditions, providing a useful tool for early water stress detection.

Introduction: The olive tree (Olea europaea L.) has cultural, economic, and environmental importance in the Mediterranean region. In the last two decades, olive cultivation has shifted from low-density to super high-density (SHD) planting systems. These systems are characterized by narrow hedgerows of low-vigor, early-bearing cultivars, allowing full mechanization. However, limited information are available on shoot growth dynamics of the olive tree under the SHD system. This study aimed to investigate the shoot growth dynamics of four olive cultivars (‘Arbequina’, ‘Coratina’, ‘Frantoio’, and ‘Urano’) by modeling the elongation of different shoot types (apical proleptic, lateral proleptic, sylleptic) under SHD conditions.Methods: A four-year field study was conducted on four olive cultivars (‘Arbequina’, ‘Coratina’, ‘Frantoio’, and ‘Urano’) grown in an SHD orchard under Mediterranean climate. Apical proleptic, lateral proleptic, sylleptic, and adventitious shoots were monitored. Logistic regression was applied to model shoot elongation, and statistical analyses were conducted to assess the influence of cultivar, shoot type, and year. Moreover, the effect of crop load and temperature on shoot growth was also evaluated.Results and Discussion: No significant difference was shown between the type of shoot and cultivar. Results indicate that single-phase logistic growth was the most common pattern, except for the lateral proleptic shoots of ‘Coratina’ and adventitious of ‘Urano’, where a second vegetative flush occurred. No correlation of Growing Degree Days with the shoot growth was observed. As confirmed in previous studies, crop load showed a negative influence on shoot elongation. Particularly for Arbequina’s adventitious shoots, Coratina’s and Frantio’s sylleptic shoots and Urano’s lateral proleptic, this trend was observed. This evidence showed the potential competition between the reproductive and vegetative cycle for assimilates. To our knowledge, this is the first report addressing the vegetative growth dynamics of four different shoot types of four distinct olive cultivars with different vigor in an SHD system. These findings are essential for optimizing cultivar-specific agricultural strategies (e.g. canopy management and irrigation) to achieve an optimal yield and sustainable cultivation. Future research will explore the vegetative growth dynamics, including other factors such as trunk diameter, Leaf Area Index, and water stress indices.