The digital revolution in agriculture has introduced data-driven decision-making,where artificial intelligence,especially machine learning(ML),helps analyze large and varied data sources to improve soil quality and cr...The digital revolution in agriculture has introduced data-driven decision-making,where artificial intelligence,especially machine learning(ML),helps analyze large and varied data sources to improve soil quality and crop growth indices.Thus,a thorough evaluation of scientific publications from 2007 to 2024 was conducted via the Scopus and Web of Science databases with the PRISMA guidelines to determine the realistic role of ML in soil health and crop improvement under the SDGs.In addition,the present review focused to identify and analyze the trends,challenges,and opportunities associated with the successful implementation of ML in agriculture.The assessment of various databases clearly revealed that ML implementation depends on crop management,while its limited potential in terms of soil health was explored.ML models,such as random forest and XGBoost,have demonstrated high accuracies of up to 99%in crop yield prediction and disease detection.Advanced ML frameworks,including the SHIDS-ADLT and EfficientNetB3,have improved soil health monitoring and plant disease classification.Irrigation management using ML has achieved over 50%water savings and irrigation efficiency by 10%-35%.These findings highlight the potential of ML to improve sustainable agricultural practices and soil health.A significant improvement discussed in this review is AutoML,which simplifies ML model implementation by automating feature selection,model selection,and hyperparameter tuning,reducing dependency on ML expertise.The integration of ML with remote sensing,Internet of Things(IoT),and big data analytics is expected to further transform the precision agriculture and real-time decisionmaking approaches to optimize resource utilization.Conclusively,the present review offers a quantitative perspective on the evolution of ML in agriculture,soil health management,crop yield prediction,and resource optimization.展开更多
Metal and metalloid pollutants severely threatens environmental ecosystems and human health,necessitating effective remediation strategies.Nanoparticle(NPs)-based approaches have gained significant attention as promis...Metal and metalloid pollutants severely threatens environmental ecosystems and human health,necessitating effective remediation strategies.Nanoparticle(NPs)-based approaches have gained significant attention as promising solutions for efficient removing heavy metals from various environmental matrices.The present review is focused on green synthesized NPs-mediated remediation such as the implementation of iron,carbon-based nanomaterials,metal oxides,and bio-based NPs.The review also explores the mechanisms of NPs interactions with heavy metals,including adsorption,precipitation,and redox reactions.Critical factors influencing the remediation efficiency,such as NPs size,surface charge,and composition,are systematically examined.Furthermore,the environmental fate,transport,and potential risks associated with the application of NPs are critically evaluated.The review also highlights various sources of metal and metalloid pollutants and their impact on human health and translocation in plant tissues.Prospects and challenges in translating NPs-based remediation from laboratory research to real-world applications are proposed.The current work will be helpful to direct future research endeavors and promote the sustainable implementation of metal and metalloid elimination.展开更多
基金supported by the Ministry of Science and Higher Education of the Russian Federation(no.FENW-2023-0008)the Strategic Academic Leadership Program of Southern Federal University,known as“Priority 2030”.
文摘The digital revolution in agriculture has introduced data-driven decision-making,where artificial intelligence,especially machine learning(ML),helps analyze large and varied data sources to improve soil quality and crop growth indices.Thus,a thorough evaluation of scientific publications from 2007 to 2024 was conducted via the Scopus and Web of Science databases with the PRISMA guidelines to determine the realistic role of ML in soil health and crop improvement under the SDGs.In addition,the present review focused to identify and analyze the trends,challenges,and opportunities associated with the successful implementation of ML in agriculture.The assessment of various databases clearly revealed that ML implementation depends on crop management,while its limited potential in terms of soil health was explored.ML models,such as random forest and XGBoost,have demonstrated high accuracies of up to 99%in crop yield prediction and disease detection.Advanced ML frameworks,including the SHIDS-ADLT and EfficientNetB3,have improved soil health monitoring and plant disease classification.Irrigation management using ML has achieved over 50%water savings and irrigation efficiency by 10%-35%.These findings highlight the potential of ML to improve sustainable agricultural practices and soil health.A significant improvement discussed in this review is AutoML,which simplifies ML model implementation by automating feature selection,model selection,and hyperparameter tuning,reducing dependency on ML expertise.The integration of ML with remote sensing,Internet of Things(IoT),and big data analytics is expected to further transform the precision agriculture and real-time decisionmaking approaches to optimize resource utilization.Conclusively,the present review offers a quantitative perspective on the evolution of ML in agriculture,soil health management,crop yield prediction,and resource optimization.
文摘Metal and metalloid pollutants severely threatens environmental ecosystems and human health,necessitating effective remediation strategies.Nanoparticle(NPs)-based approaches have gained significant attention as promising solutions for efficient removing heavy metals from various environmental matrices.The present review is focused on green synthesized NPs-mediated remediation such as the implementation of iron,carbon-based nanomaterials,metal oxides,and bio-based NPs.The review also explores the mechanisms of NPs interactions with heavy metals,including adsorption,precipitation,and redox reactions.Critical factors influencing the remediation efficiency,such as NPs size,surface charge,and composition,are systematically examined.Furthermore,the environmental fate,transport,and potential risks associated with the application of NPs are critically evaluated.The review also highlights various sources of metal and metalloid pollutants and their impact on human health and translocation in plant tissues.Prospects and challenges in translating NPs-based remediation from laboratory research to real-world applications are proposed.The current work will be helpful to direct future research endeavors and promote the sustainable implementation of metal and metalloid elimination.
