Machine Learning Approach for Prediction of Area Under Cultivation and Production for Vegetatively Propagated Crops
Vegetatively propagated crops (VPCs) such as cassava, sweet potatoes, and bananas, are a key component in ensuring food security for the low- and middle-income countries (LMICs). In agricultural planning and seed system management, it is essential to accurately predict the area under cultivation, pr...
| Autores principales: | , , , |
|---|---|
| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/172714 |
Ejemplares similares: Machine Learning Approach for Prediction of Area Under Cultivation and Production for Vegetatively Propagated Crops
- A Machine Learning Approach for Estimating sweetpotato Cultivation Areas in Uganda
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Machine learning approach to predicting Rift Valley fever disease outbreaks in Kenya
- Advanced Prediction of Rice Yield Gaps Under Climate Uncertainty Using Machine Learning Techniques in Eastern India
- Predicting climate smart agriculture (CSA) practices using machine learning: A prime exploratory survey
- Integrating APSIM model with machine learning to predict wheat yield spatial distribution