Data

Data for predicting chlorophyll content in sugarcane crops

Queensland University of Technology
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
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25912/RDF_1658292366650&rft.title=Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithm and spectral vegetation indices derived from UAV multispectral imagery&rft.identifier=10.25912/RDF_1658292366650&rft.publisher=Queensland University of Technology&rft.description=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. Acknowledgements: 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. &rft.creator=Amarasingam, Narmilan &rft.creator=Felipe, Gonzalez &rft.creator=Arachchige Surantha, Ashan Salgadoe &rft.creator=Unupen Widanelage Lahiru, Madhushanka Kumarasiri &rft.creator=Hettiarachchige Asiri, Sampageeth Weerasinghe &rft.creator=Buddhika, Rasanjana Kulasekara &rft.creator=Sugarcane Research Institute, Sri Lanka &rft.date=2022&rft.edition=1&rft.coverage=80.831879,6.404937&rft_rights=© Narmilan Amarasingam, 2022.&rft_rights=Creative Commons Attribution-NonCommercial-No Derivatives 3.0 http://creativecommons.org/licenses/by-nc-nd/4.0/&rft_subject=Sugarcane&rft_subject=Electrical engineering&rft_subject=ENGINEERING&rft_subject=Spectral Vegetation Indices&rft_subject=UAV&rft_subject=Remote Sensing&rft_subject=Multispectral Imagery&rft_subject=Aerospace engineering&rft_subject=Machine Learning&rft_subject=Chlorophyll&rft.type=dataset&rft.language=English Access the data

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Non-Derivative Licence view details
CC-BY-NC-ND

Creative Commons Attribution-NonCommercial-No Derivatives 3.0
http://creativecommons.org/licenses/by-nc-nd/4.0/

© Narmilan Amarasingam, 2022.

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Contact Information

Postal Address:
Mr Narmilan Amarasingam

narmilan.amarasingam@hdr.qut.edu.au

Full description

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.

Acknowledgements:

  • 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.

Data time period: 11 09 2021 to 11 09 2021

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80.831879,6.404937

80.831879,6.404937

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