Resultados de búsqueda - "Learning"

Early detection of plant virus infection using multispectral imaging and spatial-spectral machine learning por Peng, Y., Dallas, M.M., Ascencio‑Ibanez, J.T., Hoyer, J.S., Legg, James P., Hanley-Bowdoin, L., Grieve, B., Yin, H.

Materias: “…machine learning…”
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Motivational pathways for farmer learning behaviour in the student‐to‐farmer university outreach por Kalule, S.W., Sseguya, H., Karubanga, G., Ongeng, D.

Materias: “…learning…”
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Machine learning technique unraveled subspecies-specific ionomic variation with the preferential mineral enrichment in rice por Tiozon, Rhowell N., Buenafe, Reuben James, Jain, Vinay, Sen, Partha, Molina, Lilia R., Anacleto, Roslen, Sreenivasulu, Nese

Materias: “…machine learning…”
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DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants por Wang, Kelin, Abid, Muhammad Ali, Awais Rasheed, Crossa, José, Hearne, Sarah Jane, Huihui Li

Materias: “…learning…”
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Social learning in games: Stimulating institutional and behavior change in relation to water use in India por Falk, Thomas, Meinzen-Dick, Ruth S., Priyadarshini, Pratiti, Singh, Subrata, Mittal, Rajesh

Materias: “…learning…”
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Integrating APSIM model with machine learning to predict wheat yield spatial distribution por Kheir, A.M.S., Mkuhlani, S., Mugo, J.W., Elnashar, A., Nangia, V., Devare, Medha, Govind, A.

Materias: “…machine learning…”
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Site-specific fertilizer recommendation using data driven machine approaches enhanced wheat productivity and resource use efficiency por Liben, Feyera, Abera, Wuletawu, Ebrahim, Mohammed, Tilaye, Asmalu, Chernet, Meklit, Erkossa, Teklu, Tibebe, Degefie, Mponela, Powell, Kihara, Job Maguta, Tamene, Lulseged D.

Materias: “…machine learning…”
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Metabolomics and machine learning technique revealed that germination enhances the multi-nutritional properties of pigmented rice por Tiozon, Rhowell N., Jr., Sreenivasulu, Nese, Alseekh, Saleh, Sartagoda, Kristel June D., Usadel, Björn, Fernie, Alisdair R.

Materias: “…machine learning…”
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A machine learning algorithm for mapping small reservoirs using Sentinel-2 satellite imagery in Google Earth Engine por Siabi, Ebenezer K., Akpoti, Komlavi, Zwart, Sander J.

Materias: “…machine learning…”
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Researcher-implementer partnerships for learning and impact: Lessons from IFPRI's experiences with nutrition-sensitive programming por Bliznashka, Lilia, Sproule, Katie

Materias: “…learning…”
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Leveraging unsupervised machine learning to examine women's vulnerability to climate change por Caruso, German, Mueller, Valerie, Villacis, Alexis

Materias: “…machine learning…”
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Capacity versus incentive factors explaining research productivity: Comparative and multilevel analysis of Nigeria and Ghana agricultural research systems por Ragasa, Catherine

Materias: “…organizational learning…”
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Learning processes during regime shifts: empirical evidence from the diffusion of greywater recycling in Spain por Domènech, Laia, March, Hug, Vallès, Maria, Saur, David

Materias: “…learning…”
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Social learning and parameter uncertainty in irreversible investments: Evidence from greenhouse adoption in northern China por Wang, Honglin, Yu, Fan, Reardon, Thomas, Huang, Jikun, Rozelle, Scott

Materias: “…social learning…”
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Open distance learning for development: Lessons from strengthening research capacity on gender, crisis prevention, and recovery por Babu, Suresh Chandra, Ferguson, Jenna, Prasai, Nilam, Almoguera, Rose

Materias: “…distance learning…”
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Dynamic informative advertising of new experience goods por Saak, Alexander E.

Materias: “…learning…”
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Machine learning based rice blast model development. Report. por Selvaraj, Michael, Giraldo, Juan Camilo

Materias: “…machine learning…”
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Collaborative multi-stakeholder spaces to learn about socio-technical bundling por Puskur, Ranjitha, Nchanji, Eileen, Cosmas, Lutomia, Ketema, Dessalegn, Muteti, Thomas, Gartaula, Hom, Mukhopadhyay, Prama, Mapedza, Everisto D., Chadha, Deepali

Materias: “…learning…”
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Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach por Negussie, Kaleb Gizaw, Gebrekidan, Bisrat Haile, Wyss, Daniel, Kappas, M.

Materias: “…learning…”
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BrRacemeCounter: Mask R-CNN for Raceme Instance Segmentation por International Center for Tropical Agriculture (CIAT) Tropical Forages Program, Arrechea-Castillo, Darwin Alexis, Cardoso Arango, Juan Andres

Materias: “…machine learning…”
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