Search Results - "machine learning"

Taller de fundamentos en análisis de información y minería de datos para agricultura (Big Data) by Tzorin Herrera, Sofía, Jaimes, Diana, Gonzalez, Arturo, Arias, Diana, Pacheco, Monstserrat, Chavez Can, Mario, Llanos Herrera, Lizeth, Navarro Racines, Carlos

Subjects: “…machine learning…”
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Mapping and Monitoring Fractional Woody Vegetation Cover in the Arid Savannas of Namibia Using LiDAR Training Data, Machine Learning, and ALOS PALSAR Data by Wessels, Konrad, Mathieu, Renaud, Knox, Nichola, Main, Russell, Naidoo, Laven, Steenkamp, Karen

Subjects: “…machine learning…”
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Connecting climate science and society: reflections from early and mid-career researchers at the World Climate Research Programme Open Science Conference 2023 by Díaz, Leandro B., Gulizia, Carla, Jain, Shipra, Langendijk, Gaby S., Palanisamy, Hindumathi, Rabanal, Valentina, Charlton, Candice S., Dajuma, Alima, Donkor, Felix K., Egbebiyi, Temitope S., Kondi Akara, Ghafi, Jrrar, Amna, Siddiqui, Md Rezwan, Brahim, Yassine Ait, Angnuureng, Bapentire D., Anschütz, Anna-Adriana, Awo, Founi M., Builes-Jaramillo, Alejandro, Cobb, Alison, Diallo, Mohamadou A., Er-Retby, Houda, Fotso-Nguemo, Thierry C., Gwinneth, Benjamin, Gudoshava, Masilin, Kemgang Ghomsi, Franck E., Leyba, Inés M., Lguensat, Redouane, Mahon, Roché, Mandal, Shailendra K., Mumo, Lucia, Muzuva, Meshel, Nonki, Rodric M., Rivera, Juan A., Segnon, Alcade C., Tanimoune, Laouali I., Zauisomue, Erlich-Honest E. I., Siame, Gilbert

Subjects: “…machine learning…”
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Developing automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather dataset by Kheir, Ahmed M.S., Govind, Ajit, Nangia, Vinay, Devkota Wasti, Mina Kumari, Elnashar, Abdelrazek, Omar, Mohie, Feike, Til

Subjects: “…machine learning…”
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A scalable crop yield estimation framework based on remote sensing of solar-induced chlorophyll fluorescence (SIF) by Kira, Oz, Wen, Jiaming, Han, Jimei, McDonald, Andrew J., Barrett, Christopher B., Ortiz-Bobea, Ariel, Liu, Yanyan, You, Liangzhi, Mueller, Nathaniel D., Sun, Ying

Subjects: “…machine learning…”
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Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables by Velez, Andres Felipe, Alvarez, Cesar Ivan, Navarro, Fabian, Guzman, Diego, Bohorquez, Martha Patricia, Selvaraj, Michael Gomez, Ishitani, Manabu

Subjects: “…machine learning…”
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The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making by Akingbemisilu, Tosin Harold, Jordan, Irmgard, Asiimwe, Robert, Bodjrenou, Sam, Nabuuma, Deborah, Odongo, Nicanor, Onyango, Kevin Omondi, Teferi, Ermias, Tokeshi, Casey, Lundy, Mark, Termote, Celine

Subjects: “…machine learning…”
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Next-generation hybrid precipitation forecasts that integrate Indigenous knowledge by Sutanto, S. J., Bosdijk, J., Benedict, I., Moene, A., Milosevic, D., Ludwig, Fulco, Paparrizos, S.

Subjects: “…machine learning…”
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A systematic literature review with meta-analysis of predictive modelling of Rift Valley fever outbreaks in East Africa: Machine learning and time series approaches by Mulwa, D.F., Kazuzuru, B., Misinzo, G., Bett, Bernard K.

Subjects: “…machine learning…”
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Targeting of food aid programs: Evidence from Egypt by Mahmoud, Mai, Kurdi, Sikandra

Subjects: “…machine learning…”
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Satellite-surface-area machine-learning models for reservoir storage estimation: regime-sensitive evaluation and operational deployment at Loskop Dam, South Africa by Retief, H., Vigneswaran, Kayathri, Ghosh, Surajit, Garcia Andarcia, Mariangel, Dickens, Chris

Subjects: “…machine learning…”
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Sunpheno : a deep neural network for phenological classification of sunflower images by Bengoa Luoni, Sofía Ailin, Ricci, Riccardo, Corzo, Melanie Anahi, Hoxha, Genc, Melgani, Farid, Fernandez, Paula Del Carmen

Subjects: “…Machine Learning…”
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Estimación de los niveles nutricionales de hojas de cítricos mediante análisis no destructivo aplicando técnicas de aprendizaje automático by Miralles, Guillem, Rodríguez-Carretero, Isabel, Cubero, Sergio, Martínez, Marcelino, Mateo, Fernando, Albert, Francisco, Quinones, Ana, Blasco, José, Gómez-Sanchis, Juan

Subjects: “…machine learning…”
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En studie om hur framtida landskapsarkitektur kan påverkas av Artificiell Intelligens by Sjöfors, Martin

Subjects: “…machine learning…”

Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin by Gómez Selvaraj, Michael, Vergara, Alejandro, Montenegro, Frank, Alonso Ruiz, Henry, Safari, Nancy, Raymaekers, Dries, Ocimati, Walter, Ntamwira, Jules Bagula, Tits, Laurent, Omondi, Bonaventure Aman Oduor, Blomme, Guy

Subjects: “…machine learning…”
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CIAT-developed machine learning technology identifies agroforestry crops and other land cover types using publicly available (free) satellite imagery. by CGIAR Research Program on Climate Change, Agriculture and Food Security

Subjects: “…machine learning…”
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Prediction of urban surface water quality scenarios using hybrid stacking ensembles machine learning model in Howrah Municipal Corporation, West Bengal by Singha, Chiranjit, Bhattacharjee, Ishita, Sahoo, Satiprasad, Abdelrahman, Kamal, Uddin, Md Galal, Fnais, Mohammed S., Govind, Ajit, Abioui, Mohamed

“…In the pursuit of understanding surface water quality for sustainable urban management, we created a machine learning modeling framework that utilized Random Forest (RF), Cubist, Extreme Gradient Boosting (XGB), Multivariate Adaptive Regression Splines (MARS), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and their hybrid stacking ensemble RF (SE-RF), as well as stacking Cubist (SE-Cubist), to predict the distribution of water quality in the Howrah Municipal Corporation (HMC) area in West Bengal, India. …”
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Advancing wetland mapping in Argentina: a probalitistic approach integrating remote sensing, machine learning, and cloud computing towards sustainable ecosystem monitoring by Navarro, María Fabiana, Calamari, Noelia Cecilia, Navarro, Carlos Saúl, Enriquez, Andrea Soledad, Mosciaro, Maria Jesus, Saucedo, Griselda Isabel, Barrios, Raúl Ariel, Curcio, Matías Hernán, Dieta, Victorio, Garcia Martinez, Guillermo Carlos, Iturralde Elortegui, Maria Del Rosario Ma, Michard, Nicole Jacqueline, Paredes, Paula Natalia, Umaña, Fernando, Alday Poblete, Silvina Esther, Pezzola, Nestor Alejandro, Vidal, Claudia, Winschel, Cristina Ines, Albarracin Franco, Silvia, Behr, Santiago Javier, Cianfagna, Francisco A., Cremona, Maria Victoria, Alvarenga, Fernando Agustin, Perucca, Alba Ruth, Lopez, Astor Emilio, Miranda, Federico Waldemar, Kurtz, Ditmar Bernardo

“…This study addresses these challenges by presenting a probabilistic wetland distribution map for Argentina, inte­ grating 20 years of satellite imagery with machine learning and cloud computing technologies. Our approach introduces a comprehensive set of biophysical indices, enabling the identification of key wetland characteristics: 1) permanent or temporal surface water presence; 2) water-adapted vegetation phenology; and 3) geo­ morphology conducive to water accumulation. …”
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Artificial intelligence-based biomonitoring of water quality by Pattinson, N. B., Kuen, R.

Subjects: “…machine learning…”
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A new framework for predicting and understanding flowering time for crop breeding by Deva, Chetan, Dixon, Laura, Urban, Milan Oldřich, Ramirez‐Villegas, Julian, Droutsas, Ioannis, Challinor, Andrew J.

Subjects: “…machine learning…”
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