Resultados de búsqueda - "machine"

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…”
Enlace del recurso

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…”
Enlace del recurso

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…”
Enlace del recurso

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…”
Enlace del recurso

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…”
Enlace del recurso

Machine learning innovations for rapid assessments in wheat and rice systems in South Asia. por CGIAR Research Program on Wheat

Materias: “…machine learning…”
Enlace del recurso

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…”
Enlace del recurso

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…”
Enlace del recurso

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…”
Enlace del recurso

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…”
Enlace del recurso

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

Agricultura digital y servicios agroclimáticos: Modernizando los servicios de extensión en el sector arrocero para mejorar la adaptación al clima por Barrios Perez, Camilo

Materias: “…machine learning…”
Enlace del recurso

A comparison between support vector machine and water Cloud model for estimating crop leaf area index por Hosseini, Mehdi, McNairn, Heather, Mitchell, Scott, Robertson, Laura Dingle, Davidson, Andrew, Ahmadian, Nima, Bhattacharya, Avik, Borg, Erik, Conrad, Christopher, Dabrowska Zielinska, Katarzyna, De Abelleyra, Diego, Gurdak, Radoslaw, Kumar, Vineet, Kussul, Nataliia, Mandal, Dipankar, Rao, Y.S., Saliendra, Nicanor, Shelestov, Andrii, Spengler, Daniel, Veron, Santiago Ramón, Homayouni, Saeid, Becker Reshef, Inbal

Materias: “…Machine Learning…”
Enlace del recurso
Enlace del recurso
Enlace del recurso

Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorith... por Ajith, S., Vijayakumar, S., Elakkiya, N.

Materias: “…machine learning…”
Enlace del recurso

Updating high-resolution image dataset for the automatic classification of phenological stage and identification of racemes in Urochloa spp. hybrids with expanded images and annota... por Arrechea-Castillo, Darwin Alexis, Espitia-Buitrago, Paula, Florian-Vargas, David, Estupinan, Ronald David, Velázquez-Hernández, Riquelmer, Ruiz-Hurtado, Andres Felipe, Hernandez, Luis Miguel, Jauregui, Rosa Noemi, Cardoso, Juan Andres

Materias: “…machine learning…”
Enlace del recurso

A Machine Learning- Based Approach for the Discovery of Climate Smart Peaches por Aballay, Maximiliano Martín, Chirino, Julian Santiago, Valentini, Gabriel Hugo, Sanchez, Gerardo

Materias: “…Machine Learning…”
Enlace del recurso

Beyond pastures, look at plastic: Using Sentinel-2 imagery to map silage bags to improve understanding of cattle intensity por Fernandez, Pedro David, Gärtner, Phillipp, Nasca, Jose Andres, Rojas, Tobias, Gasparri, Nestor Ignacio

Materias: “…Machine Learning Object Detection…”
Enlace del recurso
Enlace del recurso
Enlace del recurso

Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season por Buthelezi, S., Mutanga, O., Sibanda, M., Odindi, J., Clulow, A.D., Chimonyo, Vimbayi Grace Petrova, Mabhaudhi, Tafadzwanashe

Materias: “…machine learning…”
Enlace del recurso

A systematic review of UAV applications for mapping neglected and underutilised crop species’ spatial distribution and health por Abrahams, M., Sibanda, M., Dube, T., Chimonyo, Vimbayi Grace Petrova, Mabhaudhi, Tafadzwanashe

Materias: “…machine learning…”
Enlace del recurso