Resultados de búsqueda - "Machining."

Digital Soil Mapping and Soil Quality Index Development for Malawi: Spatial Insights for Sustainable Land Management por Patil, Mukund, Choudhari, Pushpajeet L., Botoman, Lester, Agengnehu, Getachew, Desta, Gizaw

Materias: “…machine learning…”
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Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches por Gebrekidan, Bisrat Gebrekidan, Tadesse, Ephrem, Sida, Tesfaye Shiferaw

Materias: “…machine learning…”
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INFINITy : A fast machine learning-based application for human influenza A and B virus subtyping por Cacciabue, Marco Polo Domingo, Marcone, Débora Natalia

Materias: “…Machine Learning…”
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Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging por Munera, Sandra, Rodríguez-Ortega, Alejandro, Cubero, Sergio, Aleixos, Nuria, Blasco, José

Materias: “…Machine learning…”
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Machine learning model accurately predict maize grain yields in conservation agriculture systems in southern Africa por Muthoni, Francis K., Thierfelder, Christian L., Mudereri, B.T., Manda, J., Bekunda, Mateete A., Hoeschle-Zeledon, Irmgard

Materias: “…machine learning…”
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A map of global peatland extent created using machine learning (Peat-ML) por Melton, Joe R., Chan, Ed, Millard, Koreen, Fortier, Matthew, Winton, R. Scott, Martin López, Javier Mauricio, Cadillo-Quiroz, Hinsby, Kidd, Darren, Verchot, Louis V.

Materias: “…machine learning…”
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Mapping spatial distribution and geographic shifts of east African highland banana (Musa spp.) in Uganda por Ochola, D., Boekelo, B., Ven, G.W. van de, Taulya, G., Kubiriba, Jerome, Asten, Piet J.A. van, Giller, Kenneth E.

Materias: “…machine learning…”
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Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction por Yunbi Xu, Zhang Xingping, Huihui Li, Hongjian Zheng, Jianan Zhang, Olsen, Michael, Varshney, Rajeev K., Boddupalli, P.M., Qian Qian

Materias: “…machine learning…”
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The progression in developing genomic resources for crop improvement por Ruperao, P., Rangan, P., Shah, T., Thakur, V., Kalia, S., Mayes, S., Rathore, A.

Materias: “…machine learning…”
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Grow the pie, or have it? Using machine learning to impact heterogeneity in the Ultra-poor graduation model por Chowdhury, Reajul Alam, Ceballos-Sierra, Federico, Sulaiman, Munshi

Materias: “…machine learning…”
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Estimating and understanding crop yields with explainable deep learning in the Indian wheat belt por Wolanin, Aleksandra, Mateo-Garciá, Gonzalo, Camps-Valls, Gustau, Gómez-Chova, Luis, Meroni, Michele, Duveiller, Gregory, You, Liangzhi, Guanter, Luis

Materias: “…machine learning…”
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Economic AI on the move: the relationship between sugar-sweetened beverages consumption and weight gain in Kyrgyzstan por Suyunov, Alisher, Akramov, Kamiljon T.

Materias: “…machine learning…”
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Factor endowments, wage growth, and changing food self-sufficiency: Evidence from country-level panel data por Otsuka, Keijiro, Liu, Yanyan, Yamauchi, Futoshi

Materias: “…machine use…”
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High-resolution crop-type mapping in northern Ghana por Alabi, Tunrayo, Muthoni, Francis, Uponi, John, Alabi, William, Oluwaleye, Josiah

Materias: “…machine learning…”
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Taller de fundamentos en análisis de información y minería de datos para agricultura (Big Data) por Tzorin Herrera, Sofía, Jaimes, Diana, Gonzalez, Arturo, Arias, Diana, Pacheco, Monstserrat, Chavez Can, Mario, Llanos Herrera, Lizeth, Navarro Racines, Carlos

Materias: “…machine learning…”
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Mapping and Monitoring Fractional Woody Vegetation Cover in the Arid Savannas of Namibia Using LiDAR Training Data, Machine Learning, and ALOS PALSAR Data por Wessels, Konrad, Mathieu, Renaud, Knox, Nichola, Main, Russell, Naidoo, Laven, Steenkamp, Karen

Materias: “…machine learning…”
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Addressing salinity intrusion in the polders of coastal Bangladesh: predictive machine-learning modeling for strategic sluice gate operations por Behera, Abhijit, Sena, Dipaka Ranjan, Hasib, Md. R., Matheswaran, Karthikeyan, Jampani, Mahesh, Mizan, Syed Adil, Islam, Md. J., Alam, R., Mondal, M. K., Sikka, Alok

Materias: “…machine learning…”
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Connecting climate science and society: reflections from early and mid-career researchers at the World Climate Research Programme Open Science Conference 2023 por Díaz, Leandro B., Gulizia, Carla, Jain, Shipra, Langendijk, Gaby S., Palanisamy, Hindumathi, Rabanal, Valentina, Charlton, Candice S., Dajuma, Alima, Donkor, Felix K., Egbebiyi, Temitope S., Kondi Akara, Ghafi, Jrrar, Amna, Siddiqui, Md Rezwan, Brahim, Yassine Ait, Angnuureng, Bapentire D., Anschütz, Anna-Adriana, Awo, Founi M., Builes-Jaramillo, Alejandro, Cobb, Alison, Diallo, Mohamadou A., Er-Retby, Houda, Fotso-Nguemo, Thierry C., Gwinneth, Benjamin, Gudoshava, Masilin, Kemgang Ghomsi, Franck E., Leyba, Inés M., Lguensat, Redouane, Mahon, Roché, Mandal, Shailendra K., Mumo, Lucia, Muzuva, Meshel, Nonki, Rodric M., Rivera, Juan A., Segnon, Alcade C., Tanimoune, Laouali I., Zauisomue, Erlich-Honest E. I., Siame, Gilbert

Materias: “…machine learning…”
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Developing automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather dataset por Kheir, Ahmed M.S., Govind, Ajit, Nangia, Vinay, Devkota Wasti, Mina Kumari, Elnashar, Abdelrazek, Omar, Mohie, Feike, Til

Materias: “…machine learning…”
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A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF) por Kira, Oz, Wen, Jiaming, Han, Jimei, McDonald, Andrew J., Barrett, Christopher B., Ortiz-Bobea, Ariel, Liu, Yanyan, You, Liangzhi, Mueller, Nathaniel D., Sun, Ying

Materias: “…machine learning…”
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