Zobrazeno 1 - 10
of 35 655
pro vyhledávání: '"Yield Prediction"'
Autor:
Wang, Yuhan1,2 (AUTHOR), Zhang, Qian2 (AUTHOR), Yu, Feng2 (AUTHOR) yuf@agri.ac.cn, Zhang, Na1,3 (AUTHOR), Zhang, Xining2 (AUTHOR), Li, Yuchen1 (AUTHOR), Wang, Ming2 (AUTHOR), Zhang, Jinmeng2 (AUTHOR)
Publikováno v:
Agronomy. Oct2024, Vol. 14 Issue 10, p2264. 26p.
Autor:
Ye, Zhicheng1 (AUTHOR), Zhai, Xu1 (AUTHOR), She, Tianlong1 (AUTHOR), Liu, Xiaoyan1 (AUTHOR), Hong, Yuanyuan1 (AUTHOR), Wang, Lihui2 (AUTHOR), Zhang, Lili3 (AUTHOR), Wang, Qiang1 (AUTHOR) 28104@ahau.edu.cn
Publikováno v:
Agronomy. Oct2024, Vol. 14 Issue 10, p2262. 18p.
Autor:
Peng, Dailiang1,2 (AUTHOR) pengdl@aircas.ac.cn, Cheng, Enhui1,2,3 (AUTHOR) chengenhui22@mails.ucas.ac.cn, Feng, Xuxiang4 (AUTHOR) fengxx@aircas.ac.cn, Hu, Jinkang1,2,3 (AUTHOR) hujinkang21@mails.ucas.ac.cn, Lou, Zihang5 (AUTHOR) lou.zihang@zju.edu.cn, Zhang, Hongchi1,2,3 (AUTHOR) zhanghongchi23@mails.ucas.ac.cn, Zhao, Bin6 (AUTHOR) binzhao@sdau.edu.cn, Lv, Yulong1,3 (AUTHOR) lvyulong24@mails.ucas.ac.cn, Peng, Hao3,7 (AUTHOR), Zhang, Bing1,3 (AUTHOR) zhangbing@aircas.ac.cn
Publikováno v:
Remote Sensing. Oct2024, Vol. 16 Issue 19, p3613. 16p.
Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-b
Externí odkaz:
http://arxiv.org/abs/2411.03320
Publikováno v:
Agronomy. Aug2024, Vol. 14 Issue 8, p1834. 17p.
Autor:
Yokoyama, Yui1 (AUTHOR), de Wit, Allard2 (AUTHOR), Matsui, Tsutomu3 (AUTHOR), Tanaka, Takashi S. T.3,4,5 (AUTHOR) takashi@agro.au.dk
Publikováno v:
Precision Agriculture. Dec2024, Vol. 25 Issue 6, p2685-2702. 18p.
Autor:
Mukherjee, Anwesha, Buyya, Rajkumar
Federated learning has become an emerging technology for data analysis for IoT applications. This paper implements centralized and decentralized federated learning frameworks for crop yield prediction based on Long Short-Term Memory Network. For cent
Externí odkaz:
http://arxiv.org/abs/2408.02998
Autor:
Crusiol, Luís Guilherme Teixeira1 (AUTHOR) luis.crusiol@colaborador.embrapa.br, Nanni, Marcos Rafael2 (AUTHOR) mrnanni@uem.br, Sibaldelli, Rubson Natal Ribeiro1 (AUTHOR) rubson.sibaldelli@embrapa.br, Sun, Liang3 (AUTHOR) sunliang@caas.cn, Furlanetto, Renato Herrig4 (AUTHOR) re.herrigfurlane@ufl.edu, Gonçalves, Sergio Luiz1 (AUTHOR) sergio.goncalves@embrapa.br, Neumaier, Norman1 (AUTHOR) norman.neumaier@embrapa.br, Farias, José Renato Bouças1 (AUTHOR) joserenato.farias@embrapa.br
Publikováno v:
Remote Sensing. Nov2024, Vol. 16 Issue 22, p4184. 29p.
Crop yield forecasting plays a significant role in addressing growing concerns about food security and guiding decision-making for policymakers and farmers. When deep learning is employed, understanding the learning and decision-making processes of t
Externí odkaz:
http://arxiv.org/abs/2407.08274
Autor:
Aminda Amarasinghe, Ishini Sangarasekara, Nuwan De Silva, Mojith Ariyaratne, Ruwanga Amarasinghe, Jinendra Bogahawatte, Janaka Alawatugoda, Damayanthi Herath
Publikováno v:
Discover Applied Sciences, Vol 6, Iss 11, Pp 1-26 (2024)
Abstract Food sustainability is crucial aspect in achieving several United Nations (UN) Sustainable Development Goals (SDGs). By integrating advanced technologies for reliable and accurate decision-making, we can advance food sustainability and, cons
Externí odkaz:
https://doaj.org/article/88a3541ef2fc44c3bce171fcb8a41535