Resultados de búsqueda - "learning"

In-Field Forage Biomass and Quality Prediction Using Image and VIS-NIR Proximal Sensing with Machine Learning and Covariance-Based Strategies for Livestock Management in Silvopastoral Systems por Serpa Imbett, Claudia M., Gómez Palencia, Erika L., Medina Herrera, Diego A., Mejía Luquez, Jorge A., Martínez, Remberto R., Burgos Paz, William O., Aguayo Ulloa, Lorena A.

“…This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. …”
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Evaluación del rendimiento del cultivo de banano a través de la aplicación de aprendizaje automático (“Machine Learning”) en Urabá, Colombia por Gómez A., Ana M.

“…El objetivo del estudio fue diseñar un marco de trabajo para el uso de algoritmos de “Machine Learning” (ML) para el sistema agrícola bananero de Urabá, Antioquia, Colombia, y evaluar modelos de ML para predecir el rendimiento del cultivo, identificando las variables predictoras más relevantes. …”
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Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging por Urquizo Barrera, Julio Cesar, Ccopi Trucios, Dennis, Ortega Quispe, Kevin, Castañeda Tinco, Italo, Patricio Rosales, Solanch, Passuni Huayta, Jorge, Figueroa Venegas, Deyanira, Enriquez Pinedo, Lucia, Ore Aquino, Zoila, Pizarro Carcausto, Samuel

Materias: “…Machine learning…”
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Innovation platforms: synopsis of innovation platforms in agricultural research and development por Schut, Marc, Klerkx, Laurens, Kamanda, J., Sartas, Murat, Leeuwis, Cees

Materias: “…learning…”
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Pollination supply models from a local to global scale por Giménez-García, Angel, Allen-Perkins, Alfonso, Bartomeus, Ignasi, Balbi, Stefano, Knapp, Jessica L., Hevia, Violeta, Woodcock, Ben Alex, Smagghe, Guy, Miñarro, Marcos, Eeraerts, Maxime, Colville, Jonathan F., Hipólito, Juliana, Cavigliasso, Pablo, Nates-Parra, Guiomar, Herrera, José M., Cusser, Sarah, Simmons, Benno I., Wolters, Volkmar, Jha, Shalene, Freitas, Breno M., Horgan, Finbarr G., Artz, Derek R., Sidhu, Sheena, Otieno, Mark, Boreux, Virginie, Biddinger, David J., Klein, Alexandra-Maria, Joshi, Neelendra K., Stewart, Rebecca I.A., Albrecht, Matthias, Nicholson, Charlie C., O’Reilly, Alison D., Crowder, David William, Burns, Katherine L.W., Nabaes Jodar, Diego Nicolás, Garibaldi, Lucas Alejandro, Sutter, Louis, Dupont, Yoko L., Dalsgaard, Bo, da Encarnação Coutinho, Jeferson Gabriel, Lázaro, Amparo, Andersson, Georg K.S., Raine, Nigel E., Krishnan, Smitha, Dainese, Matteo, van der Wer, Wopke, Smith, Henrik G., Magrach, Ainhoa

Materias: “…pollination supply models, ecological intensification, global, pollinators, machine learning…”
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Website launch of free e-courses on banana plant health por Blomme, Guy

Materias: “…e-learning…”
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Norms, gender, and payment method affect extraction behavior in a framed field experiment on community forestry in India por Zhang, Wei, Meinzen-Dick, Ruth S., Valappanandi, Sanoop, Balakrishna, Raksha, Reddy, Hemalatha, Janssen, Marco A., Thomas, Liya, Priyadarshini, Pratiti, Kandikuppa, Sandeep, Chaturvedi, Rahul, Ghate, Rucha

Materias: “…experiential learning…”
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Policy processes and food price crises: A framework for analysis and lessons from country studies por Babu, Suresh Chandra

Materias: “…learning…”
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Colombian universities incorporate PlaSA Colombia as a pedagogical tool to promote food system education among young people por Rankin, Sara, Gonzalez, Carlos, Castillo, Johana

Materias: “…experiential learning…”
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Robustness of the RGB image-based estimation for rice above-ground biomass by utilizing the dataset collected across multiple locations por Nakajima, Kota, Saito, Kazuki, Tsujimoto, Yasuhiro, Takai, Toshiyuki, Mochizuki, Atsushi, Yamaguchi, Tomoaki, Ibrahim, Ali, Mairoua, Salifou Goube, Andrianary, Bruce Haja, Katsura, Keisuke, Tanaka, Yu

Materias: “…machine learning…”
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Technical report: Next-Generation decision support tool for data-driven fertilizer recommendations to enhance maize performance in Ethiopia por Mesfin, Tewodros, Abera, Wuletawu, Ebrahim, Mohammed, Tilaye, Amsalu, Tibebe, Degefie, Tamene, Lulseged, Adimassu, Zenebe, Liben, Feyera, Bekere, Jemal, Nazib, Nibras

Materias: “…machine learning…”
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Integrative science in practice: process perspectives from ASB, the partnership for the tropical forest margins por Tomich, Thomas P., Timmer, D.W., Velarde, S.J., Alegre, J., Areskoug, V., Cash, D.W., Cattaneo, Andrea, Ericksen, Polly J., Joshi, L., Kasyoki, J., Legg, C., Locatelli, M., Murdiyarso, Daniel, Palm, Cheryl A., Porro, R., Perazzo, A.R., Salazar Vega, A., Noordwijk, Meine van, Weise, Stephan F., White, D.

Materias: “…learning…”
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Detección de outliers en muestras de entrenamiento generadas mediante interpretación visual por Banchero, Santiago, Veron, Santiago Ramón, Petek, Mariana, Sarrailhe, Sofia, De Abelleyra, Diego

Materias: “…Machine Learning…”
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Hacer agroecología en el marco de una propuesta formativa : evaluación participativa de la cosecha del maíz Anita en la Escuela Inchausti por Filippi, Monica, Dubo, Gabriela

Materias: “…Learning…”
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El turismo rural como espacio de desarrollo territorial y de enseñanza -aprendizaje por Walter, Pablo, Aradas Diaz, Maria Elena, Bueno, Jorge Luis, Beccaria, Fabiana Rita, Mancini Lucero, Mariano Eduardo Roque

Materias: “…Learning…”
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Banana mapping in heterogenous smallholder farming systems using high-resolution remote sensing imagery and machine learning models with implications for banana bunchy top disease surveillance por Alabi, T.R., Adewopo, Julius, Duke, O.P., Kumar, P. Lava

Materias: “…machine learning…”
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What can we learn about women’s empowerment in water, sanitation, and hygiene (WASH) in Malawi?... por Ramani, Gayathri V., Faas, Simone, Chiwasa, Febbie, Chilalika, Joan, Kamwamba-Mtethiwa, Jean, Heckert, Jessica, Raghunathan, Kalyani

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Results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages (Urochloa humidicola) in three different locations in Colombia por Camelo, Rodrigo Andres, Hernandez, Luis Miguel, Cardoso, Juan Andres, Jauregui, Rosa Noemi

“…The objective of this study was to build machine learning models to predict traits such as fresh biomass (FB), dry biomass (DBM) and nutritional traits such as crude protein (CP), in Urochloa humidicola genotypes in 3 locations (Palmira, Quilichao and Villavicencio) from harvests and analytical data (direct estimation method) and measurements acquired with portable multispectral sensors.…”
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Report of a workshop to develop a monitoring, learning and evaluation framework for the ‘More Milk in Tanzania’ project, Dar-es-Saalam, Tanzania, 9-10 May 2013 por Rao, E.J.O., Baltenweck, Isabelle, Omore, Amos O.

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Session notes and action points from the Livestock and Fish theory of change-based planning, monitoring, evaluation and learning system workshop, Nairobi, 26-27 January 2016 por Vogel, I., O'Flynn, M.

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