Resultados de búsqueda - "learning"

Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data por Kebede, G., Mudereri, B.T., Abdel-Rahman, E.M., Mutanga, O., Landmann, T., Odindi, J., Motisi, N., Pinard, F., Tonnang, H.E.Z.

Materias: “…machine learning…”
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Prediction of pulse suitability in rice fallow areas using fuzzy AHP-based machine learning methods in Eastern India por Sahoo, Satiprasad, Singha, Chiranjit, Govind, Ajit

Materias: “…marchine learning…”
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Detecting cumulative effects of inputs within the flexible production function framework through LASSO shrinkage estimation: Implications for potassium fertilizer use in India por Takeshima, Hiroyuki, Kishore, Avinash

Materias: “…machine learning…”
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IITA Monitoring, Evalation, Learning, and Impact Assessment (MELIA) Unit Refreshed Strategy 2024 - 2030 por Webber, H.

Materias: “…learning…”
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Foodgravity: understand food flows using classic gravity model and explainable artificial intelligence techniques por Dusabe, B.

Materias: “…machine learning…”
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The effect of teacher training and community literacy programming on teacher and student outcomes por Chimbutane, Feliciano, Karachiwalla, Naureen, Herrera-Almanza, Catalina, Leight, Jessica, Lauchande, Carlos

Materias: “…learning…”
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Effectiveness of games for experiential learning and collaborative governance of groundwater in India por Falk, Thomas, Meinzen-Dick, Ruth S., Priyadarshini, Pratiti, Sanil, Richu, Melesse, Mequanint, Duche, Vishwambhar, Zhang, Wei, Kosec, Katrina, Carrillo, Lucia

Materias: “…experiential learning…”
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Web mapping and monitoring tools por Nelson, A.

Materias: “…e-learning…”
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Demand intelligence: an extended descriptor report for sub-indicators por Sartas, M., Konlambigue, M.

Materias: “…machine learning…”
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Learning Agrobiodiversity. The importance of agricultural biodiversity and the role of universities por Rudebjer, P.G., Schagen, Boudy van, Baena, M., Kamau, H., Sebastian, Leocadio S.

Materias: “…learning…”
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Simultaneous adoption of integrated soil fertility management technologies in the Chinyanja Triangle, Southern Africa por Mponela, Powell, Kassie, Girma T., Tamene, Lulseged D.

Materias: “…learning…”
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Redes neuronales para la identificación de enfermedades en el cultivo de tomate (Solanum lycopersicum L.) en Honduras por Erazo V., Jorge D., Uema H., Ken S.

Materias: “…Machine Learning…”
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Desarrollo de un módulo e-learning en Pago por Servicios Ambientales por Murillo M., Oscar I.

Materias: “…e- learning…”
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Deep learning for image-based cassava disease detection por Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, James P., Hughes, D.P.

Materias: “…deep learning…”
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Online fitted policy iteration based on extreme learning machines por Escandell-Montero, Pablo, Lorente, Delia, Martínez-Martínez, José M., Soria-Olivas, Emilio, Vila-Francés, Joan, Martín-Guerrero, José D.

Materias: “…Reinforcement learning…”
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Enlace del recurso

Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method por CGIAR Research Program on Climate Change, Agriculture and Food Security

Materias: “…learning…”
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Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods por García-Rodríguez, David, Catret, Pablo, Iglesias, Domingo J., Martínez, Juan J., López, Ernesto, García, Antonio

Materias: “…Deep learning…”
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Enlace del recurso

Monitoring, Evaluation and Learning (MEL) Plan for AICCRA Zambia por Kakwasha, Keagan, Mudege, Netsayi N., Cullhaj, Megi, Schuetz, Tonya, Siamudaala, Victor

Materias: “…learning…”
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Towards Operationalizing the SRF with a Suite of Indicators within a Monitoring, Evaluation and Learning Framework por CGIAR Indicator Task Force

Materias: “…learning…”
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Modelling agricultural drought: a review of latest advances in big data technologies por Houmma, I.H., El Mansouri, L., Gadal, S., Garba, M., Hadria, R.

Materias: “…machine learning…”
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