From scattered agronomic data to smarter fertilizer management decisions: A new chapter for maize productivity in Kenya
From rainfall to rice-field inundation: an event-based machine learning framework for flood early warning in data-sparse river basins
Digital map of soil organic carbon stocks (t/ha) at 0-30 cm in the Casanare flooded savannas of the Colombian Llanos (2019)
Machine learning fusion of lidar and optical data for high-precision rice canopy height estimation: A multi-method comparison
Wheat farmers in Ethiopia achieve up to 38% higher yields with machine-learning-based fertilizer recommendations
Inclusive agricultural insurance for sustainable wheat intensification as a pathway to smallholder resilience in Ethiopia
Fairgrounds in action: How the CGIAR forages research team is simplifying machine learning operations
Recent development and innovative tools for climate and hydrological data collection and analysis in the Abbay Basin
Technical report: Next-Generation decision support tool for data-driven fertilizer recommendations to enhance maize performance in Ethiopia
Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments
NIRCoreVision: A novel deep learning-based framework with GUI integration for core set selection from NIRS data using 1D CNN and k-means clustering
Uso de machine learning y un sensor de reflectancia del dosel proximal para predecir biomasa y calidad nutricional en forrajes tropicales (Urochloa humidicola)
The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making
Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annotations
Transforming Ethiopian agriculture: Validating and scaling site-specific fertilizer recommendations to 72,000+ farmers via digital advisory.
Seasonal maize yield forecasting in South and East African countries using hybrid earth observation models
Factors affecting deep learning model performance in citizen science–based image data collection for agriculture: A case study on coffee crops
