Plot level sugarcane yield estimation by machine learning on multispectral images: A case study of Bundaberg, Australia

Author:

Akbarian ShararehORCID,Jamnani Mostafa Rahimi,Xu ChengyuanORCID,Wang Weijin,Lim SamsungORCID

Publisher

Elsevier BV

Subject

Computer Science Applications,Agronomy and Crop Science,Animal Science and Zoology,Aquatic Science,Forestry

Reference64 articles.

1. Szymon M, Frieda M, Ashley T, Imogen L. Unlocking smallholder finance for sustainable agriculture in Southeast Asia. Climate Focus and WWF; 2021.

2. Integrating landsat-8 and sentinel-2 time series data for yield prediction of sugarcane crops at the block level;Rahman;Remote Sens (Basel),2020

3. Pre-harvest sugarcane yield estimation using UAV-based RGB images and ground observation;Som-ard;Sugar Tech,2018

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

5. Estimating crop yields and production by integrating the FAO crop specific water balance model with real-time satellite data and ground-based ancillary data;Reynolds;Int J Remote Sens,2000

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