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.

Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data. By scrutinizing thirteen distinct machine learning algorithms, we endeavor to pinpoint the most accurate model for categorizing Italian provinces as either above or below the national average SMR value for respiratory cancer. Furthermore, employing XAI techniques, we delineate the salient factors crucial in predicting the two classes of SMR. Through our machine learning scrutiny, we illuminate the environmental and socioeconomic factors pertinent to mortality in this disease category, thereby offering a roadmap for prioritizing interventions aimed at mitigating risk factors.

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.

Predicting phenotypes from genomic data can significantly advance agriculture. Genomic selection, which uses genome-wide DNA markers to identify individuals with high genetic value, enhances the accuracy of breeding programs. While linear models are routinely used for genomic selection (GS), machine learning (ML) models offer complementary potential. In this study, robust ML-based models were developed to predict five phenotypic traits—three related to flowering time and two to leaf number—in Arabidopsis thaliana, a model plant with a fully sequenced genome. Using explainable artificial intelligence (XAI), specifically SHapley Additive exPlanations (SHAP) values, we identified SNPs that contributed most to trait prediction. Many of these SNPs were located in or near genes known to regulate flowering and stem elongation, such as DOG1 and VIN3, supporting the biological plausibility of the model. SHAP also enabled local interpretability at the single-plant level, revealing the genotypic basis of individual predictions. Our results indicate that integrating ML with XAI improves model interpretability and provides predictive performance comparable to traditional methods. This approach confirms known genotype–phenotype relationships and highlights new candidate loci, paving the way for functional validation. The proposed methodology offers promising applications in precision breeding and translation of insights from Arabidopsis to crop species.

The increasing availability of clinical, omic, and imaging data in clinical medicine opens unprecedented opportunities to uncover hidden patterns and mechanistic insights into disease. Available tools augment clinical reasoning, equipping clinicians with new layers of understanding to support personalized treatment and transparent risk assessment. More tools are being implemented and will soon enter such a challenging field of study and application. Graph- and sequence-based Artificial Intelligence (AI) operating on these networks underpins the vision of precision medicine, enabling early diagnosis, disease staging, treatment selection, and transparent risk stratification. Crucially, eXplainable AI (XAI) methods attribute predictions to specific nodes and sub-networks (e.g., genes, proteins, metabolites, microbes, clinical features), aligning model outputs with clinical reasoning and regulatory expectations. Rather than replacing clinicians, these tools augment clinical attentiveness and insight by tailoring decisions to each patient's molecular and clinical profile. By modelling complexity in an interpretable way, AI and network science convert performance into practice. Internists must be prepared to face this rapidly growing technological challenge. Here, we explore how network-based modelling and XAI can enhance clinical reasoning by revealing emergent behaviors, stratification patterns, and novel interactions across biological systems.