Mapping aflatoxin risk using machine learning and remote sensing in East and Southern Africa

Detalles Bibliográficos
Autores principales:	Gachoki, S., Muthoni, F., Mahuku, G., Atehnkeng, J., Njeru, N., Kamau, J., Tripathi, L.
Formato:	Informe técnico
Lenguaje:	Inglés
Publicado:	2024
Materias:	modelling climate change species distribution models machine learning
Acceso en línea:	https://hdl.handle.net/10568/159927

Ejemplares similares: Mapping aflatoxin risk using machine learning and remote sensing in East and Southern Africa

Vista rápida
Predicting aflatoxin risk in maize using machine learning and satellite data in East and Southern Africa
Vista rápida
Soybean rust early warning system in East and Southern Africa
Vista rápida
Predicting aflatoxin risk in maize using machine learning and satellite data in East and Southern Africa
Vista rápida
Remote sensing and machine learning integration to detect and forecast floods in Lodwar Town, Turkwel Basin, Kenya
Vista rápida
Mapping major land use types in Nandi County using remote sensing and machine learning
Vista rápida
Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Vista rápida
Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning
Vista rápida
Integrating APSIM model with machine learning to predict wheat yield spatial distribution
Vista rápida
Text mining and machine learning reveal global determinants of food insecurity
Vista rápida
Integrating Earth Observation, Machine Learning and Citizen Science to Map Prosopis juliflora Invasion in Semi-Arid Rangelands of Kenya
Vista rápida
Which machine learning algorithm is best suited for estimating reference evapotranspiration in humid subtropical climate?
Vista rápida
Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review
Vista rápida
Predicting yields using biophysical crop simulations, machine learning and remote sensing in India
Vista rápida
Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context
Vista rápida
Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning
Vista rápida
Pixels to pasture: Using machine learning and multispectral remote sensing to predict biomass and nutrient quality in tropical grasslands
Vista rápida
Digital soil mapping using machine learning model for Kapiti in Kenya
Vista rápida
Remote sensing and machine learning for food crop production data in Africa post-COVID-19
Vista rápida
Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia
Vista rápida
Grow the pie, or have it? Using machine learning to impact heterogeneity in the Ultra-poor graduation model
Vista rápida
Addressing salinity intrusion in the polders of coastal Bangladesh: predictive machine-learning modeling for strategic sluice gate operations
Vista rápida
Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya
Vista rápida
Assessing climate resilience in rice production: Measuring the impact of the Millennium Challenge Corporation’s IWRM scheme in the Senegal River Valley using remote sensing and machine learning
Vista rápida
Reference soil groups map of Ethiopia based on legacy data and machine learning technique: EthioSoilGrids 1.0