Climate-Smart Agriculture (CSA) aims to enhance the productivity, resilience, and sustainability of farming systems in the face of climate variability and ...

A team of researchers at Rice University and Baylor College of Medicine recently developed a new strategy for identifying ...

Research from all publishers Recent studies have demonstrated significant progress in utilising machine learning for soil temperature prediction. One investigation leveraged a boosting ensemble of ...

Soil temperature is a critical parameter that influences agricultural productivity, hydrological processes and ecosystem dynamics. Recent advances in machine learning have enabled more accurate ...

Crucially, the researchers used these models to back-calculate soil cadmium thresholds based on China's food safety limit of 0.1 mg/kg for wheat grain. These newly derived thresholds—adjusted ...

The paper offers up 13 areas where machine learning can be deployed, including energy production, CO 2 removal, education, solar geoengineering, and finance. Within these fields, the possibilities ...

Ensure data quality: Data quality is critical for accurate machine learning and AI models. Choose a database that supports data integrity constraints, data validation, and data cleansing.

Quality learning is the foundation of meaningful education, shaping how students engage with and make sense of what they learn. It is not just about retaining knowledge, it is also about developing ...

Soil moisture is one of the important parameters in Earth system models. In recent years, the retrieval based on machine learning and data fusion of multisource satellite observation data has become ...

Plants face simultaneous abiotic (drought, salinity, heat) and biotic (pathogens, pests) stresses in natural and agricultural ...

Promising practices on early childhood care and education Explore UNESCO's global compendium of impactful public policies, innovative programmes and field-tested innovations in early childhood care ...