Search Results - "Learning"

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Subjects: “…machine learning…”
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