Sugarcane yields prediction at the row level using a novel cross-validation approach to multi-year multispectral images-Reference-Cited by-同舟云学术

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Sugarcane yields prediction at the row level using a novel cross-validation approach to multi-year multispectral images

Published:2022-07 Issue: Volume:198 Page:107024
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Akbarian Sharareh^ORCID,Xu Chengyuan,Wang Weijin,Ginns Stephen,Lim Samsung

Funder

Queensland Government

Department of Agriculture and Fisheries, Queensland Government

Sugar Research Australia

Department of Agriculture, Australian Government

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference62 articles.

1. Spectral wheat yield prediction modeling using SPOT satellite imagery and leaf area index;Aboelghar;Arab. J. Geosci.,2014

2. Akbarian, S., Xu, C.Y., Lim, S., 2020. Analysis on the effect of spatial and spectral resolution of different remote sensing data in sugarcane crop yield study. In: ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., Copernicus Publications. pp. 655–661.

3. Comparison of crop canopy reflectance sensors used to identify sugarcane biomass and nitrogen status;Amaral;Precis. Agric.,2015

4. Determining iron content in Mediterranean soils in partly vegetated areas, using spectral reflectance and imaging spectroscopy;Bartholomeus;Int. J. Appl. Earth Observ. Geoinform.,2007

5. Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI;Bégué;J. Remote Sens.,2010

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