Resultados de búsqueda - "Machining."

Using Sentinel-2 Satellite Images to Estimate Traits of Forage Grasslands por Zeiner, Niklas

Materias: “…Support vector machines…”

From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a Peruvian Andean highlands: a machine learning modeling approach por Carbajal, Mariella, Ramirez, David A., Turin Canchaya, Cecilia Claudia, Schaeffer, Sean M., Konkel, Julie, Ninanya, Johan, Rinza, Javier, De Mendiburu, Felipe, Zorogastua, Percy, Villaorduña, Liliana, Quiroz, Roberto

Materias: “…Support vector machine…”
Enlace del recurso
Enlace del recurso

Climate change-induced reduction in agricultural land suitability of West-Africa’s inland valley landscapes por Akpoti, Komlavi, Groen, T., Dossou-Yovo, Elliott Ronald, Kabo-bah, A. T., Zwart, Sander J.

Materias: “…machine learning…”
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Tolerance to spittlebugs (Aeneolamia varia) in Urochloa spp. and Megathyrsus maximus grasses: A dataset for plant damage phenotyping por Ruiz-Hurtado, Andres Felipe, Espitia-Buitrago, Paula, Hernández, Luis Miguel, Jauregui, Rosa N., Cardoso, Juan Andres

Materias: “…machine learning…”
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A global spectral library to characterize the world’s soil por Viscarra Rossel, Raphael A., Behrens, T, Ben-Dor, E., Brown, D.J., Demattê, JAM, Shepherd, Keith D., Shi, Z, Stenberg, B, Stevens, A, Adamchuk, V, Aïchik, H, Barthèsl, BG, Bartholomeus, H.M., Bayer, AD, Bernoux, Martial, Böttchero, K, Brodský, L, Du, CW, Chappell, A, Fouad, Y, Genot, V, Gómez, C., Grunwald, S., Gubler, A, Hedley, CB, Knadel, M, Morrás, HJM, Nocita, M, Ramírez Lopez, L., Roudier, P., Rufasto Campos, EM, Sanborn, P, Sellitto, VM, Sudduth, KA, Rawlins, BG, Walter, C, Winowiecki, Leigh Ann, Hong, SY, Ji, W

Materias: “…machine learning…”
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Relevant loci for milk production in dairy cattle, obtained by machine learning algorithms por Raschia, Maria Agustina, Ríos, Pablo J., Maizon, Daniel Omar, Demitrio, Daniel Arturo, Poli, Mario Andres

Materias: “…Machine Learning…”
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Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin por Gómez Selvaraj, Michael, Vergara, Alejandro, Montenegro, Frank, Alonso Ruiz, Henry, Safari, Nancy, Raymaekers, Dries, Ocimati, Walter, Ntamwira, Jules Bagula, Tits, Laurent, Omondi, Bonaventure Aman Oduor, Blomme, Guy

Materias: “…machine learning…”
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Innovative use of satellite data from remote sensing and supervised learning to reduce the impact of drought on crop production por CGIAR Research Program on Wheat

Materias: “…machine learning…”
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Predicting optical water quality indicators from remote sensing using machine learning algorithms in tropical highlands of Ethiopia por Leggesse, E.S., Zimale, Fasikaw A., Sultan, D., Enku, T., Srinivasan, R., Tilahun, Seifu A.

Materias: “…machine learning…”
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ICTforAg 2024: Virtual day session. Monitoring food security trends in digital spaces: A multi-platform approach to nowcast food insecurity por Carneiro, Bia, Tucci, Giulia, Resce, Giuliano, Johnson, Kiersten, Caravaggio, Nicola

Materias: “…machine learning…”
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Identificación de zonas afectadas por sales en el Centro Sur de Córdoba usando Google Earth Engine por Gentili, Nicolás, Bocco, Joaquín, Micheloud, Elizabeth, Videla Mensegue, Horacio Rogelio, Córdoba, Mariano

Materias: “…Machine Learning…”
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Relación entre jerarquía social, comportamiento y producción en vacas lecheras en un tambo robótico pastoril = Relationship between social hierarchy, behaviour, and milk production... por Millapán, Luis Orlando, Rossi, José Luis, Taverna, Miguel Angel, Cipriotti, Pablo Ariel

Materias: “…Milking Machines…”
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Can call detail records provide insights into women's empowerment? A case study from Uganda por Slavchevska, Vanya, Tyszler, Marcelo, Burra, Dharani Dhar, Seymour, Greg, Sementsov, Denys, Lierde, Astrid van, King, Brian

Materias: “…machine learning…”
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Machine learning innovations for rapid assessments in wheat and rice systems in South Asia. por CGIAR Research Program on Wheat

Materias: “…machine learning…”
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Rift Valley fever (RVF) project on Zooniverse, a citizen science platform that allows registered volunteers to identify animals in landscapes from drone imagery por CGIAR Research Program on Agriculture for Nutrition and Health

Materias: “…machine learning…”
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Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability por El Bilali, A., Brouziyne, Youssef, Attar, O., Lamane, H., Hadri, A., Taleb, A.

Materias: “…machine learning…”
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Transforming Ethiopian agriculture: Validating and scaling site-specific fertilizer recommendations to 72,000+ farmers via digital advisory. por Desta, Lulseged Tamene, Mesfin, Tewodros, Abera, Wuletawu, Degefie, Tibebe, Ebrahim, Mohammed, Tilaye, Asmalu, Chernet, Meklit Tariku, Merga, Feyera, Desalegn, Temesgen, Yitaferu, Birru Woldetsadik

Materias: “…machine learning…”
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Predicting Risk of Ammonia Exposure in Broiler Housing: Correlation with Incidence of Health Issues por Barbosa, Leonardo V. S., da-Silva-Lima, Nilsa D., Granja-Barrios, Juliana-de-Souza, de-Moura, Daniella J., Estellés, Fernando, Ramón-Moragues, Adrián, Calvet-Sanz, Salvador, Villagrá, Arantxa

Materias: “…machine learning…”
Enlace del recurso
Enlace del recurso

Ensemble machine learning prediction of drivers affecting rice and wheat yield, greenhouse gas emissions, and yield-scaled emissions in Bangladesh por CGIAR Research Program on Climate Change, Agriculture and Food Security

Materias: “…machine learning…”
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Assessing the biophysical factors affecting irrigation performance in rice cultivation using remote sensing derived information por Sawadogo, A., Dossou-Yovo, Elliott Ronald, Kouadio, L., Zwart, Sander J., Traoré, F., Gundogdu, K. S.

Materias: “…machine learning…”
Enlace del recurso