Survey on Machine Learning and Deep Learning Techniques for Agriculture Land-Reference-Cited by-同舟云学术

Survey on Machine Learning and Deep Learning Techniques for Agriculture Land

Published:2021-10-18 Issue:6 Volume:2 Page:
ISSN:2662-995X
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Singh Gurwinder^ORCID,Sethi Ganesh Kumar^ORCID,Singh Sartajvir^ORCID

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42979-021-00929-6.pdf

Reference104 articles.

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2. Ahmadlou M, et al. Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks. J Flood Risk Manag. 2021;14(1):1–22. https://doi.org/10.1111/jfr3.12683.

3. Aznar-sánchez JA, et al. Worldwide research trends on sustainable land use in agriculture. Land Use Policy. 2019;87:1–15.

4. do Bendini HN, et al. Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series. Int J Appl Earth Obs Geoinformation. 2019;82:1–10.

5. Benedetti P, et al. M 3 fusion : a deep learning architecture for satellite data fusion. IEEE J Sel Top Appl Earth Observ Remote Sens. 2018. https://doi.org/10.1109/JSTARS.2018.2876357.

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