Resultados de búsqueda - "algorithm"

Artificial intelligence and agricultural management: Opportunities and risks por Jimenez, Daniel, Muller, Anna, Rhys, Manners, Mariangel, Garcia

“…Improving Generative AI Algorithms: • Highlight the risk of AI model hallucinations…”
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Bringing together measurements and data science for better nitrous oxide emission accounting in data-poor regions por Harris, E., Barthel, M., Leitner, Sonja, Ouma, T., Agredazywczuk, P., Otinga, A., Njoroge, R., Oduor, Collins, Oluoch, K. C.

“…Low-cost sensing is particularly suited in direct soil gas measurements, where N2O concentrations and variability are much higher than in free air. Specialised algorithms are needed to estimate fluxes based on soil gas measurements. …”
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The Compound Risk Framework: Assessing fragility and key drivers of food insecurity in Haiti, Mozambique and Ghana por Minoarivelo, Onivola Henintsoa, Craparo, Alessandro, Basel, Ashleigh Megan, Nguyen, Kien Tri, Ouedraogo, Yacouba, Douglas, Wesley, Melly, Brigitte, Dao, Hoa, Carneiro, Bia, Pacillo, Grazia, Laderach, Peter

“…The framework offers a holistic perspective on compound risk assessment by drawing on comprehensive data sources, FEWS NET analyses, and employing advanced techniques, such as natural language processing, digital platform monitoring and supervised machine learning algorithms. The geographic focus of this report is three fragile and conflict-prone countries: Mozambique, Haiti and Ghana.…”
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High-resolution global map of smallholder and industrial closed-canopy oil palm plantations por Descals, A., Wich, S., Meijaard, E., Gaveau, D.L.A., Peedell, S., Szantoi, Z.

“…The characteristic backscatter response of closed-canopy oil palm stands in Sentinel-1 and the ability of CNN to learn spatial patterns, such as the harvest road networks, allowed the distinction between industrial and smallholder plantations globally (overall accuracy =98.52±0.20 %), outperforming the accuracy of existing regional oil palm datasets that used conventional machine-learning algorithms. The user's accuracy, reflecting commission error, in industrial and smallholders was 88.22 ± 2.73 % and 76.56 ± 4.53 %, and the producer's accuracy, reflecting omission error, was 75.78 ± 3.55 % and 86.92 ± 5.12 %, respectively. …”
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AgWise: Modular decision support tools integrating data, models, and AI for site-specific agronomy and soil health management recommendations por Abera, Wuletawu, Mkuhlani, Siyabusa, Tibebe, Degefie, Assefa, Feben, Leroux, Louise, Seid, Jemal, Srivastava, Amit, Aguilar Ariza, Andres, Devkota Wasti, Mina, Ibrahim, Ali, Kouadio, Louis, Liben, Feyera, Llanos Herrera, Lizeth, Cherenet, Meklit, Corbeels, Marc, Kihara, Job, Vanlauwe, Bernard

“…The document is not intended as a technical manual or implementation guide. Detailed algorithms, parameterizations, and workflows will be described in complementary technical documents and scientific publications.…”
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Recent Land Use and Land Cover Change Dynamics in the Gran Chaco Americano por Banchero, Santiago, De Abelleyra, Diego, Veron, Santiago Ramón, Mosciaro, Maria Jesus, Arévalos, Fabiana, Volante, Jose Norberto

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Land use and land cover change and system level analysis to guide sustainable intensification efforts in mixed crop-livestock farming system por Tesfaye, Getachew, Tibebe, Degefie, Abera, Wuletawu, Tamene, Lulseged D.

“…The analysis was performed using Landsat 8 satellite imagery and random forest classification algorithms in the Google Earth Engine environment. Accordingly, agricultural land was identified as the dominant LULC type in the study area. …”
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Exposure patterns and the risk factors of Crimean Congo hemorrhagic fever virus amongst humans, livestock and selected wild animals at the human/livestock/wildlife interface in Isi... por Mukhaye, Eugine, Akoko, James M., Nyamota, Richard, Mwatondo, Athman, Muturi, Mathew, Nthiwa, D., Kirwa, Lynn J., Bargul, J.L., Abkallo, Hussein M., Bett, Bernard K.

“…For instance, the existing CCHF surveillance measures could be enhanced by incorporating algorithms that simulate disease risk based on the environmental factors identified in the study. …”
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Aplicación móvil para el análisis de imágenes de raíces de alfalfa en cámaras de crecimiento por Micheletto, Matías, Zubiaga, Luciano, Reinoso, Omar Juan, Renzi Pugni, Juan Pablo

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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

“…In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. …”
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Improving Latin American soil information database for digital soil mapping enhances its usability and scalability por Diaz Guadarrama, Sergio, Lizarazo, Ivan, Guevara, Mario, Angelini, Marcos Esteban, Araujo Carrillo, Gustavo A., Argeñal, Jainer, Armas, Daphne, Balta, Rafael A., Bolivar, Adriana, Bustamante, Nelson, Dart, Ricardo O., Dell Acqua, Martín, Encina, Arnulfo, Figueredo, Hernán, Fontes, Fernando, Gutierrez Diaz, Joan S., Gimenez, Wilmer, Rodriguez, Dario Martin, Schulz, Guillermo, Tenti Vuegen, Leonardo Mauricio

“…The updated profiles were segmented using algorithms for quantitative pedology to estimate the spatial variability. …”
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Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium por Thenkabail, Prasad S., Biradar, Chandrashekhar M., Noojipady, P., Dheeravath, Venkateswarlu, Li, Yuan Jie, Velpuri, Naga Manohar, Gumma, Murali K., Gangalakunta, O.R.P., Turral, Hugh, Xueliang Cai, Vithanage, Jagath, Schull, M.A., Dutta, R.

“…Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. …”
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Detección automática del riego por pivote central a partir de imágenes satelitales de alta resolución en el norte de Buenos Aires (2016-2023) por Barrionuevo, Nestor, Havrylenko, Sofía Beatriz, Sepulcri, Maria Gabriela, Casella, Alejandra An, Espindola, Aime

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Delineación y codificación de cuencas hidrográficas en la Patagonia Austral por Diaz, Boris Gaston, Giménez, Melisa, Almonacid, Leandro, Gaspari, Fernanda Julia, Bertinat, Mariano, Peri, Pablo Luis

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Utilización de índices de vegetación satelitales para predecir niveles de rendimiento de Vicia villosa Roth = Use of satellite vegetation indices to predict yield level of Vicia vi... por Marini, Mario Fabian, D'Amico, María Belén, Calandrini, Guillermo Luis, Renzi Pugni, Juan Pablo, Chantre Balacca, Guillermo Ruben

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Control genético y ambiental de los determinantes fisiológicos del llenado, secado y calidad de granos de maíz en genotipos destinados para grano o silaje por Chazarreta, Yésica Daniela

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Determinación de Requerimientos Hídricos para Avena Forrajera (Avena sativa L.) y Raigrás (Lolium perenne) en la Sabana de Bogotá (Colombia) por Terán Chaves, César, Murcia Contreras, Gilberto, García Prats, Alberto

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En jämförelse mellan två datorprogram för utbytesberäkningar por Samuelsson, Johan

“…The differences were marginal and that is probably more because of small differences in the forest characteristics between the regions rather than poor algorithms in the programs. Damaged sawlogs which get degraded to pulpwood are the objectives of studies 3 and 4. …”
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Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia por Lamos Díaz, Henry, Puentes Garzón, David Esteban, Zarate Caicedo, Diego Alejandro

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Effects of urbanization on avian diversity and human-nature interactions in tropical environments por Awoyemi, A.G.

“…By leveraging machine learning algorithms and remotely sensed data, this approach can help reduce the labor, expense, and investigator error associated with manual data collection. …”
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