This systematic review explores the use of digital twins (DT) for sustainable agricultural water management. DTs simulate real-time agricultural environments, enabling precise resource allocation, predictive maintenance, and scenario planning. AI enhances DT performance through machine learning (ML) and data-driven insights, optimizing water usage. In this study, from an initial pool of 48 papers retrieved from well-known databases such as Scopus and Web of Science, etc., a rigorous eligibility criterion was applied, narrowing the focus to 11 pertinent studies. This review highlights major disciplines where DT technology is being applied: hydroponics, aquaponics, vertical farming, and irrigation. Additionally, the literature identifies two key sub-applications within these disciplines: the simulation and prediction of water quality and soil water. This review also explores the types and maturity levels of DT technology and key concepts within these applications. Based on their current implementation, DTs in agriculture can be categorized into two functional types: monitoring DTs, which emphasize real-time response and environmental control, and predictive DTs, which enable proactive irrigation management through environmental forecasting. AI techniques used within the DT framework were also identified based on their applications. These findings underscore the transformative role that DT technology can play in enhancing efficiency and sustainability in agricultural water management. Despite technological advancements, challenges remain, including data integration, scalability, and cost barriers. Further studies should be conducted to explore these issues within practical farming environments.

Climate change presents an unprecedented global challenge, demanding collective action to both mitigate its effects and adapt to its consequences. Soil health and function are profoundly impacted by climate change, particularly evident in the sensitivity of soil microbial respiration to warming, known as Q10. Q10 measures the rate of microbial respiration’s increase with a temperature rise of 10 degrees Celsius, playing a pivotal role in understanding soil carbon dynamics in response to climate change. Leveraging machine learning techniques, particularly explainable artificial intelligence (XAI), offers a promising avenue to analyze complex data and identify biomarkers crucial for developing innovative climate change mitigation strategies. This research aims to evaluate the extent to which chemical, physical, and microbiological soil characteristics are associated with high or low Q10 values, utilizing XAI approaches. The Extra Trees Classifier algorithm was employed, yielding an average accuracy of $0.923\pm 0.009$, an average AUCROC of $0.964\pm 0.004$, and an average AUCPRC of $0.963\pm 0.006$. Additionally, through XAI techniques, we elucidate the significant features contributing to the prediction of Q10 classes. The XAI analysis shows that the temperature sensitivity of soil respiration increases with microbiome variables but decreases with non-microbiome variables beyond a threshold. Our findings underscore the critical role of the soil microbiome in predicting soil Q10 dynamics, providing valuable insights for developing targeted climate change mitigation strategies.

This article explores the significant impact that artificial intelligence (AI) could have on food safety and nutrition, with a specific focus on the use of machine learning and neural networks for disease risk prediction, diet personalization, and food product development. Specific AI techniques and explainable AI (XAI) are highlighted for their potential in personalizing diet recommendations, predicting models for disease prevention, and enhancing data-driven approaches to food production. The article also underlines the importance of high-performance computing infrastructures and data management strategies, including data operations (DataOps) for efficient data pipelines and findable, accessible, interoperable, and reusable (FAIR) principles for open and standardized data sharing. Additionally, it explores the concept of open data sharing and the integration of machine learning algorithms in the food industry to enhance food safety and product development. It highlights the METROFOOD-IT project as a best practice example of implementing advancements in the agri-food sector, demonstrating successful interdisciplinary collaboration. The project fosters both data security and transparency within a decentralized data space model, ensuring reliable and efficient data sharing. However, challenges such as data privacy, model interoperability, and ethical considerations remain key obstacles. The article also discusses the need for ongoing interdisciplinary collaboration between data scientists, nutritionists, and food technologists to effectively address these challenges. Future research should focus on refining AI models to improve their reliability and exploring how to integrate these technologies into everyday nutritional practices for better health outcomes.

Optimizing nitrogen (N) fertilization is essential for sustaining durum wheat yield and grain quality while reducing the environmental impacts associated with N over-application. Hyperspectral sensing provides a rapid and non-destructive approach for monitoring crop N status. However, high-dimensional data, phenology-dependent spectral responses, and spatial autocorrelation in field measurements limit robust nitrogen classification and interpretation. This study evaluated hyperspectral-based nitrogen status classification in durum wheat under Mediterranean field conditions and identified key spectral regions using explainable artificial intelligence. A field experiment was conducted in Southern Italy using ten N fertilization rates (0–180 kg N ha⁻¹). Canopy reflectance was acquired at the booting and heading stages from georeferenced sampling locations. Three nitrogen stratification strategies (binary Low–High, Extreme, and three-level) were evaluated using Random Forest, SVM-RBF, and XGBoost classifiers. Model performance was assessed using spatially independent Leave-One-Plot-Out cross-validation at both the sample and plot levels, with plot-level predictions derived through majority voting. Classification robustness was strongly influenced by the stratification strategy and phenological stage. The binary Low–High stratification achieved the highest sample-level accuracy, with a maximum of 0.78 at booting (SVM-RBF) and 0.75 at heading (SVM-RBF), whereas the Extreme stratification produced intermediate performance, with maximum accuracies of 0.73 at booting (SVM-RBF) and 0.63 at heading (XGBoost). Plot-level aggregation improved performance, reaching up to 0.90 at booting and 1.00 at heading. SHAP analysis highlighted red, red-edge, and near-infrared wavelengths as the dominant contributors, with increased reliance on longer wavelengths at the heading. Overall, explainable machine learning provides a robust framework for hyperspectral nitrogen monitoring in durum wheat.

This study investigated the predictive performance of three regression models—Gradient Boosting (GB), Random Forest (RF), and XGBoost—in forecasting mortality due to endocrine, nutritional, and metabolic diseases across Italian provinces. Utilizing a dataset encompassing air pollution metrics and socio-economic indices, the models were trained and tested to evaluate their accuracy and robustness. Performance was assessed using metrics such as coefficient of determination ($r^2$), mean absolute error (MAE), and root mean squared error (RMSE), revealing that GB outperformed both RF and XGB, offering superior predictive accuracy and model stability ($r^2$ = 0.55, MAE = 0.17, and RMSE = 0.05). To further interpret the results, SHAP (SHapley Additive exPlanations) analysis was applied to the best-performing model to identify the most influential features driving mortality predictions. The analysis highlighted the critical roles of specific pollutants, including benzene and socio-economic factors such as life quality and instruction, in influencing mortality rates. These findings underscore the interplay between environmental and socio-economic determinants in health outcomes and provide actionable insights for policymakers aiming to reduce health disparities and mitigate risk factors. By combining advanced machine learning techniques with explainability tools, this research demonstrates the potential for data-driven approaches to inform public health strategies and promote targeted interventions in the context of complex environmental and social determinants of health.