Canales - Operations research and machine learning to manage risk and optimize production practices in agriculture: good and bad experience :: Servicio de Descubrimiento de la UNA

Ejemplares similares: Operations research and machine learning to manage risk and optimize production practices in agriculture: good and bad experience

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Site-specific fertilizer recommendation using data driven machine learning enhanced wheat productivity and resource use efficiency
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Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review
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Predicting climate smart agriculture (CSA) practices using machine learning: A prime exploratory survey
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Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India
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Machine Learning Approach for Prediction of Area Under Cultivation and Production for Vegetatively Propagated Crops
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Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
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MusaDeepMosaic: Development of a machine learning genomic mosaic classifier tool.
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Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia
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Digital soil mapping using machine learning model for Kapiti in Kenya
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Machine learning based rice blast model development. Report.
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Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods
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Leveraging unsupervised machine learning to examine women's vulnerability to climate change
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Mekong River Delta crop mapping using a machine learning approach
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Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes
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Addressing salinity intrusion in the polders of coastal Bangladesh: predictive machine-learning modeling for strategic sluice gate operations
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Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya
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PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia
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Integrating APSIM model with machine learning to predict wheat yield spatial distribution
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Remote sensing and machine learning for food crop production data in Africa post-COVID-19
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Using machine learning tools for salinity forecasting to support irrigation management and decision-making in a polder of coastal Bangladesh
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From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach
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Text mining and machine learning reveal global determinants of food insecurity
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Explainable machine learning driven nutrient recommendation for maize production in Malawi
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Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier

Autor: Cock, James H.

Autor: Jiménez, Daniel