Data for predicting chlorophyll content in sugarcane crops
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
Spectral Vegetation Indices
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
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
Name: Mr Narmilan Amarasingam
Date record created:
Date record modified:
Published - Open Access