Resultados de búsqueda - Artificial intelligence Forecasting.

Combining seasonal forecasts and artificial intelligence to improve agronomy at scale por CGIAR Research Program on Climate Change, Agriculture and Food Security

“…We have used the approach developed by Dorado et al. (2019) (see link provided) combined with seasonal forecasts (Esquivel et al., 2018) to produce a proof of concept where we combine seasonal forecasts and artificial intelligence prediction models for optimizing agronomic practices.…”
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Integration of Artificial Intelligence in Seasonal to Sub‑Seasonal Forecasting Systems in West Africa and the Sahel por Houngnibo, Mandela C., Ali, Abdou, Assoumana, Boubacar Toukal, Minoungou, Bernard, Segnon, Alcade Christel, Zougmore, Robert Bellarmin

Materias: “…artificial intelligence…”
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Fast-forwarding plant breeding with deep learning-based genomic prediction por Gao, Shang, Yu, Tingxi, Rasheed, Awais, Wang, Jiankang, Crossa, Jose, Hearne, Sarah, Li, Huihui

Materias: “…artificial intelligence…”
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Chinese artificial intelligence in Africa: Digital colonisation or liberalisation? por Tinarwo, Joseph, Babu, Suresh Chandra

“…In this era of globalisation and progressive digitalisation, artificial intelligence (AI) has become an integral part of our everyday lives. …”
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An AI-driven River Salinity Forecast and Advisory System for Climate Resilient Agriculture in Coastal Bangladesh por Matheswaran, Karthikeyan, Behera, Abhijit, Sena, Dipaka Ranjan, Jampani, Mahesh, Hasib, Md Raqubul, Mondal, Manoranjan K.

Materias: “…artificial intelligence…”
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Decomposing USDA ending stocks forecast errors por Goyal, Raghav, Adjemian, Michael K., Glauber, Joseph W., Meyer, Seth

Materias: “…artificial intelligence…”
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WaterCopilot: a water management AI virtual assistant for the Limpopo River Basin Digital Twin - user guide V0 202410 por Vickneswaran, Keerththanan, Retief, H., Padilha, R., Dickens, Chris, Silva, Paulo, Ghosh, Surajit, Garcia Andarcia, Mariangel

Materias: “…artificial intelligence…”
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Building Climate Resilience in Agrifood Systems Through AI- Powered Agromet Advisory Services por Kumar, Shalander, Rao, K.P.C., Kumar, G. Kishore

Materias: “…artificial intelligence…”
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Change point-driven interrupted time series and machine learning models for forecasting Indian food grain production por Chitikela, Gayathri, Rathod, Santosha, Vijayakumar, S.

Materias: “…artificial intelligence…”
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Unleashing the potential of underutilized datasets to improve agricultural decision-making through comprehensive data analysis: An example of rice crop manager (RCM) dataset por Gakhar, Shalini, Sharma, Sheetal

Materias: “…artificial intelligence…”
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Optimizing Rice Crop Manager Odisha: AI-Driven Yield Prediction to compliment Extension using legacy data por Gakhar, Shalini, Bharti, Preeti

Materias: “…artificial intelligence…”
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Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis por Ndour, Adama, Blasch, Gerald, Valente, João, Bisrat Gebrekidan, Sida, Tesfaye Shiferaw

Materias: “…artificial intelligence…”
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SukhaRakshak AI - anticipatory drought intelligence for India por Amarnath, Giriraj, Kapoor, Sanya, Bhatpuria, Dhyey, Padhee, Suman Kumar, Rao, K. V., Sudhishri, S., Sikka, Alok

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Early warning AI agent for pest, disease, and weed in Viet Nam por Kim, Soonho, Liu, Yanyan

Materias: “…artificial intelligence…”
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Herramientas digitales para el fortalecimiento de los servicios de información climática en Honduras por Calderon, Santiago, Ayes, Irma, Guzman, Daniel, Nasayo, Carlos, Montes, Carlo, Lopez, Sebastian, Argeñal, Francisco, Ramirez, Julian, Sotelo, Steven

Materias: “…artificial intelligence…”
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Report of CGIAR’s Digital Transformation Accelerator side event–AI-powered innovation: Acceleration research for agri-food system transformation por Ojanji, Wandera, Dhulipala, Ram

“…CGIAR’s Digital Transformation Accelerator Side Event: Digital innovations for advancing agri-food systems research, held during Science Week 2025 showcased how artificial Intelligence (AI) is reshaping agri-food systems research, innovation, and advisory services, particularly in the Global South. …”
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A flexible and practical approach for real-time weed emergence prediction based on Artificial Neural Networks por Chantre Balacca, Guillermo Ruben, Vigna, Mario Raul, Renzi Pugni, Juan Pablo, Blanco, Anibal Manuel

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Estimating and understanding crop yields with explainable deep learning in the Indian wheat belt por Wolanin, Aleksandra, Mateo-Garciá, Gonzalo, Camps-Valls, Gustau, Gómez-Chova, Luis, Meroni, Michele, Duveiller, Gregory, You, Liangzhi, Guanter, Luis

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Digital Twin for management of water resources in the Limpopo River Basin: a concept por Garcia Andarcia, Mariangel, Dickens, Chris, Silva, Paulo, Matheswaran, Karthikeyan, Koo, Jawoo

“…The Digital Twin will provide an attractive and easy-to-use interface for users to intuitively understand large volumes of data and modelling results for timely management decisions, and to simulate the impact of these decisions on the complex river basin ecosystem before they are put into action. This includes an artificial intelligence (AI) tool to interrogate and visualize key actionable data and forecasts. …”
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A bibliometric analysis of solar energy forecasting studies in Africa por Zwane, N., Tazvinga, H., Botai, C., Murambadoro, M., Botai, J., Wit, J. de, Mabasa, B., Daniel, S., Mabhaudhi, Tafadzwanashe

“…Three emerging topics were identified from the thematic map analysis— namely, “solar irradiance”, “artificial intelligence” and “clear sky”, which implies that researchers are paying attention to solar irradiance, using modelling techniques that incorporate machine learning techniques. …”
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