2022-07-20T14:51:00 n3367

Data for predicting chlorophyll content in sugarcane crops

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A study was conducted in a sugarcane field located at the Sugar Research Institute (SRI), Udawalawa, Sri Lanka, with various fertiliser applications over the entire growing season from 2020 to 2021.

An unmanned Aerial Vehicle (UAV) with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertiliser status in the sugarcane field in this study. The different machine learning (ML) algorithms were developed using ground-truthing data of chlorophyll content and UAV-derived spectral vegetation indices to forecast sugarcane chlorophyll content.


  • Narmilan Amarasingam conducted the UAV flight mission, analysis and prepared the manuscript for final submission as a corresponding author.
  • Professor Felipe Gonzalez provided overall supervision and contributed to the writing and editing.
  • Surantha provided the technical guidance to conduct the UAV flight mission and research design and provide the feedbacks on draft manuscript.
  • Lahiru, Sampageeth and Buddhika developed the experimental design in the field and carried out the fieldwork for ground sample collection.

Location of data collection



Narmilan, A.; Gonzalez, F.; Salgadoe, A.S.A.; Kumarasiri, U.W.L.M.; Weerasinghe, H.A.S.; Kulasekara, B.R. Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery. Remote Sens. 2022, 14, 1140. http://dx.doi.org/http://dx.doi.org/10.3390/rs14051140

Research areas

Machine Learning
Aerospace engineering
Multispectral Imagery
Remote Sensing
Spectral Vegetation Indices
Electrical engineering

Cite this collection

Amarasingam, Narmilan; Felipe, Gonzalez; Arachchige Surantha, Ashan Salgadoe; Unupen Widanelage Lahiru, Madhushanka Kumarasiri; Hettiarachchige Asiri, Sampageeth Weerasinghe; Buddhika, Rasanjana Kulasekara; Sugarcane Research Institute, Sri Lanka; (2022): Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithm and spectral vegetation indices derived from UAV multispectral imagery. Queensland University of Technology. (Dataset) https://doi.org/10.25912/RDF_1658292366650

Partner institution

Sugarcane Research Institute, Sri Lanka https://sugarres.lk/

Data file types

Microsoft Excel (.xlsx) and Multispectral orthomosaic image (.tiff).


Creative Commons Attribution-NonCommercial-No Derivatives 4.0 (CC-BY-NC-ND)


© Narmilan Amarasingam, 2022.

Dates of data collection

From 2021-09-11 to 2021-09-11


Has association with
Felipe Gonzalez  (Researcher)


Name: Mr Narmilan Amarasingam


Date record created:
Date record modified:
Record status:
Published - Open Access