Search Results - "machine learning"

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

Subjects: “…Machine Learning…”
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Multi-hazard risk mapping using machine learning by 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 by Gumma, Murali K., Panjala, Pranay, Ismail, Mohammed, Kumar, Pavan, Harawa, Rebbie

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

Subjects: “…machine learning…”
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Revealing sequence variation patterns in rice with machine learning methods by 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 by Castro, Wilson, Tene, Baldemar, Castro, Jorge, Guivin, Alex, Ruesta Campoverde, Nelson Asdrubal, Avila George, Himer

Subjects: “…Machine learning…”
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Machine learning and big data techniques for satellite-based rice phenology by 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 by 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 by 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 by Caruso, German, Mueller, Valerie, Villacis, Alexis

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

Subjects: “…machine learning…”
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Machine learning reveals spatiotemporal genome evolution in Asian rice domestication by 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 by 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

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

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

Subjects: “…machine learning…”
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Sample Earth: Machine-Learning–Ready Land-Cover Reference Dataset by 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 by Liben, Feyera, Kihara, Job, Abera, Wuletawu, AbebeMesfin, Tewodrofins, Homann-kee Tui, Sabine, Munthali, Moses, Munthali, Chandiona, Nalivata, Patson, Mzumara, Edward, Tuimene, Lulseged

Subjects: “…machine learning…”
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A Machine Learning- Based Approach for the Discovery of Climate Smart Peaches by Aballay, Maximiliano Martín, Chirino, Julian Santiago, Valentini, Gabriel Hugo, Sanchez, Gerardo

Subjects: “…Machine Learning…”
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Machine learning in space and time for modelling soil organic carbon change by 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 by Alliance of Bioversity International and CIAT

Subjects: “…machine learning…”
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