Search Results - "Learning"

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

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

Subjects: “…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 by Takeshima, Hiroyuki, Kishore, Avinash

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

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

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

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

Subjects: “…experiential learning…”
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Web mapping and monitoring tools by Nelson, A.

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

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

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

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

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

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

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

Subjects: “…Reinforcement learning…”
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Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method by CGIAR Research Program on Climate Change, Agriculture and Food Security

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

Subjects: “…Deep learning…”
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Monitoring, Evaluation and Learning (MEL) Plan for AICCRA Zambia by Kakwasha, Keagan, Mudege, Netsayi N., Cullhaj, Megi, Schuetz, Tonya, Siamudaala, Victor

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

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

Subjects: “…machine learning…”
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