Resultados de búsqueda - "Learning"

A novel combination of the 'adaptive research' methodology with the Learning Alliance approach por CGIAR Research Program on Rice

Materias: “…learning…”
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E-learning course on Mainstreaming Biodiversity for Food and Nutrition (available in English, Portuguese and Turkish) por CGIAR Research Program on Agriculture for Nutrition and Health

Materias: “…learning…”
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Five e-learning courses (FAO e-learning academy) on mechanization business models por CGIAR Research Program on Maize

Materias: “…learning…”
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Using phenomics to identify and integrate traits of interest for better-performing common beans: A validation study on an interspecific hybrid and its Acutifolii parents por Conejo Rodriguez, Diego Felipe, Urban, Milan Oldřich, Santaella, Marcela, Gereda, Javier Mauricio, Contreras, Aquiles Darghan, Wenzl, Peter

Materias: “…machine learning…”
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Mekong River Delta crop mapping using a machine learning approach por Ghosh, Surajit, Wellington, Michael, Holmatov, Bunyod

Materias: “…machine learning…”
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Predicting Wheat Yield Gap and its Determinants in Rainfed Mediterranean Climate of Morocco: Using Ground Information, Satellite Images and Machine Learning por Devkota, Krishna, Devkota Wasti, Mina Kumari, Bouasria, Abdelkrim

Materias: “…machine learning…”
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Operations research and machine learning to manage risk and optimize production practices in agriculture: good and bad experience por Cock, James H., Jiménez, Daniel, Dorado, Hugo, Oberthür, Thomas

Materias: “…machine learning…”
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Community voices on climate, peace and security: a social learning approach por Medina, Leonardo, Pacillo, Grazia, Hellin, Jonathan James, Bonatti, Michelle

Materias: “…social learning…”
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Expert consultation workshops: Developing a common vision of climate-related security risks por Medina, Leonardo, Belli, Anna, Caroli, Giulia, Schapendonk, Frans

Materias: “…social learning…”
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Digital functional phenomic descriptors featured from machine learning-driven image-based phenotyping improve the accuracy of classic descriptors: A case study on Arachis spp. and... por Conejo Rodriguez, F., Gonzalez Guzman, J., Ramirez, Gil J., Urban, Milan Oldřich, Wenzl, Peter

Materias: “…machine learning…”
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Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes por Nantongo, J.S., Serunkuma, E., Burgos, C., Nakitto, M., Davrieux, F., Ssali, R.T.

Materias: “…machine learning…”
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Organizational and institutional barriers to the effectiveness of public expenditures: The case of agricultural research investments in Nigeria and Ghana por Ragasa, Catherine

Materias: “…institutional learning…”
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Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review por Pokhariyal, Shweta, Patel, N. R., Govind, Ajit

Materias: “…machine learning…”
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Rural innovation systems and networks: Findings from a study of Ethiopian smallholders por Spielman, David J., Davis, Kristin E., Negash, Martha, Ayele, Gezahegn

Materias: “…social learning…”
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Are experience and schooling complementary? evidence from migrants' assimilation in the Bangkok labor market por Yamauchi, Futoshi

Materias: “…experiential learning…”
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RGB Image Dataset of Urochloa Hybrids for High-Throughput Phenotyping and Artificial Intelligence Applications por Arrechea-Castillo, Darwin Alexis, Espitia Buitrago, Paula Andrea, Florian Vargas, David Alberto, Estupinan Arboleda, Ronald David, Velazquez Hernandez, Riquelmer, Camelo Munevar, Rodrigo Andres, Cardoso Arango, Juan Andres

Materias: “…machine learning…”
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Social capital and geography of learning: roles in accelerating the spread of integrated pest management por Palis, Florencia G., Morin, Stephen, Hossain, Mahabub

Materias: “…learning…”
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Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR por Herath, Achini, Tiozon, Rhowell Jr., Kretzschmar, Tobias, Sreenivasulu, Nese, Mahon, Peter, Butardo, Vito

Materias: “…machine learning…”
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Machine learning algorithms translate big data into predictive breeding accuracy por Crossa, José, Montesinos-Lopez, Osval A., Costa-Neto, Germano, Vitale, Paolo, Martini, Johannes W.R., Runcie, Daniel E., Fritsche-Neto, Roberto, Montesinos-Lopez, Abelardo, Perez-Rodriguez, Paulino, Gerard, Guillermo S., Dreisigacker, Susanna, Crespo-Herrera, Leonardo A., Saint Pierre, Carolina, Lillemo, Morten, Cuevas, Jaime, Bentley, Alison R., Ortiz, Rodomiro

Materias: “…machine learning…”
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Advancing food security: rice yield estimation framework using time-series satellite data & machine learning por Tiwari, Varun, Thorp, Kelly, Tulbure, Mirela G., Gray, Joshua, Kamruzzaman, Mohammad, Krupnik, Timothy J., Sankarasubramanian, A., Ardon, Marcelo

Materias: “…machine learning…”
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