Search Results - "artificial intelligence"

Harmonizing Agricultural Data for AI-Powered Intelligent Agricultural Systems Advisory Tool (ISAT) in Ethiopia by Kumar, Kishore G., Dessalegn, Olika, Folorunso, Akinseye, Maila, Nagaraju, Ugendar, K., Mohan, Divya, Gopi, T., Kumar, Shalander

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The future of food, land and water systems in the face of digital technologies by Veeger, Marieke, Jiménez, Daniel, Garcia Andarcia, Mariangel

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Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction by Yunbi Xu, Zhang Xingping, Huihui Li, Hongjian Zheng, Jianan Zhang, Olsen, Michael, Varshney, Rajeev K., Boddupalli, P.M., Qian Qian

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Impressed with ChatGPT's agricultural knowledge? CGIAR's open access effort (probably)* enabled it by Koo, Jawoo, Devare, Medha, King, Brian

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The progression in developing genomic resources for crop improvement by Ruperao, P., Rangan, P., Shah, T., Thakur, V., Kalia, S., Mayes, S., Rathore, A.

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Large language models and agricultural extension services by Tzachor, Asaf, Devare, Medha, Richards, Catherine, Pypers, Pieter, Ghosh, Aniruddha, Koo, Jawoo, Johal, S., King, Brian

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Terra-i+ webtool: Simplifying agroforestry sustainability monitoring by Nguyen, Phuong Minh, Luong, Phuong Thi, Vantalon, Thibaud, Reymondin, Louis, Talsma, Tiffany, Phan, Trong Van, Nguyen Thi, Thuy Thu, Bunn, Christian

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Development of AI methods for seed yield prediction using RGB images: Progress Report by Arrechea, Darwin A., Cardoso Arango, Juan Andrés

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Digital AgroClimate Advisory (DACA): A web and mobile digital application for provision of actionable crop and climate recommendations along agricultural value chains by Kagabo, Desire, Mvuyibwami, Patrick, Byandaga, Livingstone, Singh, Manjari

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Terra-i+ website: Satellite based solution for Agroforestry Supply Chain Sustainability (https://www.terra-iplus.org/) by Vantalon, Thibaud, Talsma, Tiffany, Reymondin, Louis, Nguyen, Phuong Minh, Luong, Phuong Thi, Bunn, Christian, Nguyen Thi, Thuy Thu, Phan, Trong Van, Castro Llanos, Fabio Alexander, Paz Garcia, Paula Andrea, Pérez Escobar, Jorge Andres, Tello Dagua, Jhon Jairo

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Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria by Ibrahim, Ali, Senthilkumar, Kalimuthu, Saito, Kazuki

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Feasibility of using an artificial intelligence-based telephone application for dietary assessment and nudging to improve the quality of food choices of female adolescents in Vietn... by Braga, Bianca C., Nguyen, Phuong Hong, Tran, Lan Mai, Hoang, Nga Thu, Bannerman, Boateng, Doyle, Frank, Folson, Gloria, Gangupantulu, Rohit, Karachiwalla, Naureen, Kolt, Bastien, McCloskey, Peter, Palloni, Giordano, Tran, Trang Huyen Thi, Trơưng, Duong Thuy Thi, Hughes, David, Gelli, Aulo

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CGIAR Research Initiative on Plant Health: Annual Technical Report 2023 by CGIAR Initiative on Plant Health

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

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Economic AI on the move: the relationship between sugar-sweetened beverages consumption and weight gain in Kyrgyzstan by Suyunov, Alisher, Akramov, Kamiljon T.

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WaterCopilot: a water management AI virtual assistant for the Limpopo River Basin Digital Twin - technical guide by Vickneswaran, Keerththanan, Retief, H., Padilha, R., Dickens, Chris, Silva, Paulo, Garcia Andarcia, Mariangel

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CGIAR Research Initiative on Plant Health: Annual Technical Report 2024 by CGIAR Initiative on Plant Health

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AI-powered risk assessment tools for agribusinesses: transforming investment-readiness and climate advisory in Africa by Dahl, Hauke, Peterson, Nathanial Richard, Muoki, Nicole

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From Science to Action: Exploring the Potential of Artificial Intelligence to Strengthen Climate and Agricultural Research Capacities in Africa by Smith Ruiz, Paulina, Grossi, Amanda, Chipunza, Unity

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EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models by Yu, Tingxi, Zhang, Hao, Chen, Shoukun, Gao, Shang, Liu, Ze, Wang, Jiankang, Crossa, Jose, Montesinos-Lopez, Osval Antonio, Hearne, Sarah, Li, Huihui

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