AI-Driven Prediction of Sugarcane Quality Attributes Using Satellite Imagery-Reference-Cited by-同舟云学术

AI-Driven Prediction of Sugarcane Quality Attributes Using Satellite Imagery

Published:2024-03-26 Issue:3 Volume:26 Page:741-751
ISSN:0972-1525
Container-title:Sugar Tech
language:en
Short-container-title:Sugar Tech

Author:

Canata Tatiana Fernanda^ORCID,Júnior Marcelo Rodrigues Barbosa,de Oliveira Romário Porto,Furlani Carlos Eduardo Angeli,da Silva Rouverson Pereira

Funder

FAPESP

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12355-024-01399-9.pdf

Reference35 articles.

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3. Bolfe, E.L., L.A.C. Jorge, I.D. Sanches, A. Luchiari Júnior, C.C. da Costa, D.C. Victoria, R.Y. Inamasu, et al. 2020. Precision and digital agriculture: Adoption of technologies and perception of Brazilian farmers. Agriculture 10: 653. https://doi.org/10.3390/agriculture10120653.

4. Canata, T.F., M.C. Wei, L.F. Maldaner, and J.P. Molin. 2021. Sugarcane yield mapping using high-resolution imagery data and machine learning technique. Remote Sensing 13: 232. https://doi.org/10.3390/rs13020232.

5. Carneiro, M., M.F.F. de Oliveira, S.L.H. Almeida, A.L.B. Filho, C.E.A. Furlani, G.S. Rolim, A.S. Ferraudo, and R.P. da Silva. 2022. Biophysical characteristics of soybean estimated by remote sensing associated with artificial intelligence. Bioscience Journal 38: e38024. https://doi.org/10.14393/BJ-v38n0a2022-55925.