Resultados de búsqueda - "machine learning"

Imputación de genotipos faltantes mediante algoritmos de machine learning = Imputation of missing genotypes using machine learning algorithms por Raschia, Maria Agustina, Rios, Pablo Javier, Cordoba, Marcela Elisabet, Caffaro, María Eugenia, Donzelli, María Valeria, Maizon, Daniel Omar, Demitrio, Daniel Arturo, Poli, Mario Andres

Materias: “…Machine Learning…”
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Multi-hazard risk mapping using machine learning por Adounkpe, Peniel, Ghosh, Surajit, Amarnath, Giriraj

“…This study maps out Ghana’s multi-hazard risk of flood and drought by using machine learning (ML) models for susceptibility analysis, socioeconomic survey for vulnerability analysis and population density for exposure analysis. …”
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In-season crop monitoring using machine learning algorithms por Gumma, Murali K., Panjala, Pranay, Ismail, Mohammed, Kumar, Pavan, Harawa, Rebbie

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Machine learning based rice blast model development. Report. por Selvaraj, Michael, Giraldo, Juan Camilo

Materias: “…machine learning…”
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Revealing sequence variation patterns in rice with machine learning methods por Bohnert, Regina, Zeller, Georg, Clark, Richard M., Childs, Kevin L., Ulat, Victor, Stokowski, Renee, Ballinger, Dennis, Frazer, Kelly, Cox, David, Bruskiewich, Richard, Buell, C. Robin, Leach, Jan, Leung, Hei, McNally, Kenneth L., Weigel, Detlef, Rätsch, Gunnar

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Mango varietal discrimination using hyperspectral imaging and machine learning por Castro, Wilson, Tene, Baldemar, Castro, Jorge, Guivin, Alex, Ruesta Campoverde, Nelson Asdrubal, Avila George, Himer

Materias: “…Machine learning…”
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Machine learning and big data techniques for satellite-based rice phenology por Aguilar-Ariza, Andrés

“…Two optical moderate-resolution missions were combined to detect growth phases. Three machine-learning approaches (random forest, support vector machine, and gradient boosting trees) were trained with multitemporal NDVI data. …”
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Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh por Belton, Ben, Haque, Mohammad Mahfujul, Ali, Hazrat, Nejadhashemi, Amir Pouyan, Hernández, Ricardo, Khondker, Murshed-E-Jahan, Ferriby, Hannah

“…The presentation detailed the use of machine learning techniques to extract information from freely available satellite images and estimate the area of waterbodies used for aquaculture in seven districts in southern Bangladesh, one of country’s most important aquaculture zones producing fish for domestic markets and crustaceans for export. …”
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Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh por Belton, Ben, Haque, Mohammad Mahfujul, Ali, Hazrat, Nejadhashemi, Amir Pouyan, Hernández, Ricardo, Khondker, Murshed-E-Jahan, Ferriby, Hannah

“…The presentation detailed the use of machine learning techniques to extract information from freely available satellite images and estimate the area of waterbodies used for aquaculture in seven districts in southern Bangladesh, one of country’s most important aquaculture zones producing fish for domestic markets and crustaceans for export.…”
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Leveraging unsupervised machine learning to examine women's vulnerability to climate change por Caruso, German, Mueller, Valerie, Villacis, Alexis

Materias: “…machine learning…”
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Digital soil mapping using machine learning model for Kapiti in Kenya por Paliwal, Ambica

Materias: “…machine learning…”
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Machine learning reveals spatiotemporal genome evolution in Asian rice domestication por Ohyanagi, Hajime, Goto, Kosuke, Negrão, Sónia, Wing, Rod A., Tester, Mark A., McNally, Kenneth L., Bajic, Vladimir B., Mineta, Katsuhiko, Gojobori, Takashi

“…Here we show the genome-wide introgressive region (IR) map of Asian rice, by utilizing 4,587 accession genotypes with a stable outgroup species, particularly at the finest resolution through a machine learning-aided method. The IR map revealed that 14.2% of the rice genome consists of IRs, including both wide IRs (recent) and narrow IRs (ancient). …”
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Machine learning algorithms translate big data into predictive breeding accuracy por Crossa, José, Montesinos-Lopez, Osval A., Costa-Neto, Germano, Vitale, Paolo, Martini, Johannes W.R., Runcie, Daniel E., Fritsche-Neto, Roberto, Montesinos-Lopez, Abelardo, Perez-Rodriguez, Paulino, Gerard, Guillermo S., Dreisigacker, Susanna, Crespo-Herrera, Leonardo A., Saint Pierre, Carolina, Lillemo, Morten, Cuevas, Jaime, Bentley, Alison R., Ortiz, Rodomiro

Materias: “…machine learning…”
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A Machine Learning Approach for Estimating sweetpotato Cultivation Areas in Uganda por Rajendran, S.

Materias: “…machine learning…”
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Text mining and machine learning reveal global determinants of food insecurity por Carneiro, Bia, Resce, Giuliano, Caravaggio, Nicola, Santangelo, Agapito Emanuele, Ruscica, Giosue, Tucci, Giulia, Pacillo, Grazia, Eilert, Gary, Läderach, Peter, Coffey, Kevin

Materias: “…machine learning…”
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Sample Earth: Machine-Learning–Ready Land-Cover Reference Dataset por Vantalon, Thibaud, Luong, Phuong Thi, Perez Escobar, Jorge Andres, Tello Dagua, Jhon Jairo, Phan, Trong Van, Nguyen, Hang, Hong Nguyen, Hoa Nguyen, Reymondin, Louis

“…This combined approach of expert interpretation, localized training, and structured data management ensured a high-quality, consistent, and machine-learning–ready dataset suitable for land-cover mapping and model training workflows.…”
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Explainable machine learning driven nutrient recommendation for maize production in Malawi por Liben, Feyera, Kihara, Job, Abera, Wuletawu, AbebeMesfin, Tewodrofins, Homann-kee Tui, Sabine, Munthali, Moses, Munthali, Chandiona, Nalivata, Patson, Mzumara, Edward, Tuimene, Lulseged

Materias: “…machine learning…”
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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…”
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Machine learning in space and time for modelling soil organic carbon change por Heuvelink, Gerard B.M., Angelini, Marcos Esteban, Poggio, Laura, Bai, Zhanguo, Batjes, Niels H., van den Bosch, Rik, Bossio, Deborah, Estella, Sergio, Lehmann, Johannes, Olmedo, Guillermo Federico, Sanderman, Jonathan

“…We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. …”
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Terra-i + Using machine learning to manage impacts of coffee production in Ocotepeque por Alliance of Bioversity International and CIAT

Materias: “…machine learning…”
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