Resultados de búsqueda - "machine"

Can remote sensing enable a Biomass Climate Adaptation Index for agricultural systems? por Ferguson, Amy, Murray, Catherine, Tessema, Yared Mesfin, McKeown, Peter C., Reymondin, Louis, Loboguerrero Rodriguez, Ana María, Talsma, Tiffany, Allen, Brenden, Jarvis, Andy, Golden, Aaron, Spillane, Charles

Materias: “…machine learning…”
Enlace del recurso

A protocol for measuring disease resistance to Cassava Bacterial Blight, using machine-learning image analysis por Pardo, Juan Manuel, Jiménez Polo, Jenyfer, Rodríguez, Rafael, Leiva Sandoval, Ana Maria, Cuéllar, Wilmer Jose

Materias: “…machine learning…”
Enlace del recurso

Digitally improving the identification of aquatic macroinvertebrates for indices used in biomonitoring por Koen, R. C. J., Koen, F. J., Pattinson, N. B., Dickens, Chris W. S., Graham, P. M.

Materias: “…machine learning…”
Enlace del recurso

Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure por Gokool, S., Mahomed, M., Brewer, K., Naiken, V., Clulow, A., Sibanda, M., Mabhaudhi, Tafadzwanashe

Materias: “…machine learning…”
Enlace del recurso

Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning por Mcbride, Linden, Barrett, Christopher B., Browne, Christopher, Hu, Leiqiu, Liu, Yanyan, Matteson, David S., Sun, Ying, Wen, Jiaming

Materias: “…machine learning…”
Enlace del recurso

Remote sensing and machine learning for food crop production data in Africa post-COVID-19 por Ly, Racine, Dia, Khadim, Diallo, Mariam A.

Materias: “…machine learning…”
Enlace del recurso

Multivariate random forest prediction of poverty and malnutrition prevalence por Browne, Chris, Matteson, David S., McBride, Linden, Hu, Leiqiu, Liu, Yanyan, Sun, Ying, Wen, Jiaming, Barrett, Christopher B.

Materias: “…machine learning…”
Enlace del recurso

Research-policy linkages: Empirical evidence from agroeconomic research in India por Balaji, S. J., Babu, Suresh Chandra, Pal, Suresh

Materias: “…machine learning…”
Enlace del recurso

Predicting high-magnitude, low-frequency crop losses using machine learning: An application to cereal crops in Ethiopia por Mann, Michael L., Malik, Arun S., Warner, James

Materias: “…machine learning…”
Enlace del recurso

Zambia: Systematic analysis of domestic production and world market shocks por Mukashov, Askar, Diao, Xinshen, Jones, Eleanor, Thurlow, James

Materias: “…machine learning…”
Enlace del recurso

Bangladesh: Systematic analysis of domestic production and world market shocks por Mukashov, Askar, Jones, Eleanor, Thurlow, James

Materias: “…machine learning…”
Enlace del recurso

Papua New Guinea: Systematic analysis of domestic production and world market shocks por Mukashov, Askar, Dorosh, Paul A., Schmidt, Emily, Thurlow, James

Materias: “…machine learning…”
Enlace del recurso

Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software por Crossa, José, Martini, Johannes W.R., Vitale, Paolo, Perez-Rodriguez, Paulino, Costa-Neto, Germano, Fritsche-Neto, Roberto, Runcie, Daniel E., Cuevas, Jaime, Toledo, Fernando H., Huihui Li, De Vita, Pasquale, Gerard, Guillermo S., Dreisigacker, Susanne, Crespo-Herrera, Leonardo A., Saint Pierre, Carolina, Bentley, Alison R., Lillemo, Morten, Ortiz, Rodomiro, Montesinos-Lopez, Osval A., Montesinos-López, Abelardo

Materias: “…machine learning…”
Enlace del recurso

Parametric and machine learning approaches to examine yield differences between control and treatment considering outliers and statistical biases: The case of insect resistant/herb... por Falck-Zepeda, José B., Zambrano, Patricia, Sanders, Arie, Trabanino, Carlos Rogelio

Materias: “…machine learning…”
Enlace del recurso

Integrating crop models and machine learning for projecting climate change impacts on crops in data-limited environments por Alimagham, Seyyedmajid, van Loon, Marloes P, Ramirez Villegas, Julian, Berghuijs, Herman N.C., Rosenstock, Todd Stuart, van Ittersum, Martin K.

Materias: “…machine learning…”
Enlace del recurso

Early detection of infection by Penicillium digitatum in oranges using hyperspectral imaging and machine learning por Castillo-Gironés, Salvador, Gómez-Sanchis, Juan, López-Chulia, Marina, Guirao, Alberto, Palou, Lluís, Souza, Ricardo-Lima-de, Blasco, José

Materias: “…machine learning…”
Enlace del recurso

Implementing cloud computing for the digital mapping of agricultural soil properties from high resolution UAV multispectral imagery por Pizarro Carcausto, Samuel Edwin, Pricope, Narcisa G., Figueroa Venegas, Deyanira Antonella, Carbajal Llosa, Carlos Miguel, Quispe Huincho, Miriam Rocío, Vera Vilchez, Jesús Emilio, Alejandro Méndez, Lidiana Rene, Achallma Mendoza, Lino, González Tovar, Izamar Estrella, Salazar Coronel, Wilian, Loayza, Hildo, Cruz Luis, Juancarlos Alejandro, Arbizu Berrocal, Carlos Irvin

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

Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems por Brewer, K., Clulow, A.D., Sibanda, M., Gokool, S., Naiken, V., Mabhaudhi, Tafadzwanashe

Materias: “…machine learning…”
Enlace del recurso

Climate variability and extremes impact on seasonal occurrence patterns of malaria cases in Senegal [Abstract only] por Jampani, Mahesh, Panjwani, Shweta, Ghosh, Surajit, Sambou, Mame Henriette Astou, Amarnath, Giriraj

Materias: “…machine learning…”
Enlace del recurso

PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia por Mabilangan, Abigail, Mkuhlani, Siyabusa, Saito, Kazuki

Materias: “…machine learning…”
Enlace del recurso