Integration of maximum crop response with machine learning regression model to timely estimate crop yield-Reference-Cited by-同舟云学术

Integration of maximum crop response with machine learning regression model to timely estimate crop yield

Published:2021-07-03 Issue:3 Volume:24 Page:474-483
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Zhou Qiming¹^ORCID,Ismaeel Ali¹

Affiliation:

1. Department of Geography, Hong Kong Baptist University, Hong Kong, China

Funder

The research is supported by the Natural Science Foundation of China

Natural Science Foundation of China (NSFC) General Research Grant

Hong Kong Research Grant Council (RGC) General Research Fund

Publisher

Informa UK Limited

Subject

Computers in Earth Sciences,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2021.1957723

Reference33 articles.

1. Impact of temperature and rainfall on rice production in Punjab, Pakistan

2. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics

3. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review

4. Craig, M., and D. Atkinson. 2013. “A Literature Review of Crop Area Estimation”, Report by UN-FAO, Rome, Italy. http://www.fao.org/fileadmin/templates/ess/documents/meetings_and_workshops/GS_SAC_2013/Improving_methods_for_crops_estimates/Crop_Area_Estimation_Lit_review.pdf

5. Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

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