Search Results - "machine"

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

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

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

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

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

Subjects: “…machine learning…”
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Multivariate random forest prediction of poverty and malnutrition prevalence by Browne, Chris, Matteson, David S., McBride, Linden, Hu, Leiqiu, Liu, Yanyan, Sun, Ying, Wen, Jiaming, Barrett, Christopher B.

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

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

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

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

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

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Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software by 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

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

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

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

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Implementing cloud computing for the digital mapping of agricultural soil properties from high resolution UAV multispectral imagery by 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

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

Subjects: “…machine learning…”
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Climate variability and extremes impact on seasonal occurrence patterns of malaria cases in Senegal [Abstract only] by Jampani, Mahesh, Panjwani, Shweta, Ghosh, Surajit, Sambou, Mame Henriette Astou, Amarnath, Giriraj

Subjects: “…machine learning…”
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PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia by Mabilangan, Abigail, Mkuhlani, Siyabusa, Saito, Kazuki

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A robust model based on root morphological and anatomical features to distinguish high and low methane emission rice varieties through machine learning approaches by Roy, Ripon Kumar, Hosseiniyan Khatibi, Seyed Mahdi, Kim, Sung-Ryul, Trijatmiko, Kurniawan Rudi, Diaz, Maria Genaleen Q., Ocampo, Eureka Teresa M., Hernandez, Jose E., Pirmoradi, Saeed, Henry, Amelia, Kohli, Ajay

Subjects: “…machine learning…”
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