Zobrazeno 1 - 10
of 3 241
pro vyhledávání: '"Yield Prediction"'
Publikováno v:
Information Processing in Agriculture, Vol 11, Iss 4, Pp 476-487 (2024)
Early crop yield prediction provides critical information for Precision Agriculture (PA) procedures, policymaking, and food security. The availability of Remote Sensing (RS) datasets and Machine Learning (ML) approaches improved the prediction of sug
Externí odkaz:
https://doaj.org/article/dca1b28fc66b4c838a309da7ddad9d3f
Autor:
Mohammad Amin Razavi, A. Pouyan Nejadhashemi, Babak Majidi, Hoda S. Razavi, Josué Kpodo, Rasu Eeswaran, Ignacio Ciampitti, P.V. Vara Prasad
Publikováno v:
Artificial Intelligence in Agriculture, Vol 14, Iss , Pp 99-114 (2024)
In this study, we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in Senegal. We analyze how these features influence crop yie
Externí odkaz:
https://doaj.org/article/f35fb07613a84a0386b8d284587d2b6f
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract New and simple crop yield prediction methods are expected to be developed owing to the increasing environmental stress caused by climate change. Algorithms of machine learning could be a powerful tool for predicting crop yield; however, the
Externí odkaz:
https://doaj.org/article/20a5d62368fd403f95044f2d49b558f7
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
Autor:
J. Logeshwaran, Durgesh Srivastava, K. Sree Kumar, M. Jenolin Rex, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-40 (2024)
Abstract Background The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various f
Externí odkaz:
https://doaj.org/article/f8d604b247c54f08b5bc3e2fa7ff9a36
Publikováno v:
AgriEngineering, Vol 6, Iss 3, Pp 2283-2305 (2024)
In this study, we explored the efficacy of random forest algorithms in downscaling CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation data to predict Argane stand traits. Nonparametric regression integrated original
Externí odkaz:
https://doaj.org/article/871e3f27dc2148b3822e76f5bd6eec45
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
The ability to accurately predict the yields of different crop genotypes in response to weather variability is crucial for developing climate resilient crop cultivars. Genotype-environment interactions introduce large variations in crop-climate respo
Externí odkaz:
https://doaj.org/article/21155201ecb44c23bfba1178d9c51386
Autor:
Jianxi Huang, Jianjian Song, Hai Huang, Wen Zhuo, Quandi Niu, Shangrong Wu, Han Ma, Shunlin Liang
Publikováno v:
Science of Remote Sensing, Vol 10, Iss , Pp 100146- (2024)
Combining the advantages of crop growth models and remote sensing observations, data assimilation (DA) has emerged as a vital tool for crop growth monitoring and early-season crop yield forecasting. As an increasing number of related studies have bee
Externí odkaz:
https://doaj.org/article/a17f9d5f3f314d098d82ccbcbf8c9373
Publikováno v:
Smart Agricultural Technology, Vol 9, Iss , Pp 100671- (2024)
Field-scale corn yield prediction before harvest can assist farmers in better organizing their resources. Machine learning-based pipelines for analyzing remote sensing imagery offer an efficient solution to this problem. However, the cost of data acq
Externí odkaz:
https://doaj.org/article/214224ad4b4d42dd872929a76f53ec0d
Publikováno v:
Journal of Agrometeorology, Vol 26, Iss 4 (2024)
Externí odkaz:
https://doaj.org/article/c3d43c321d4f43a495754328e9a43e28