2022-12-12T11:30:00 n21355

Image dataset for detecting sugarcane white leaf disease using Deep learning

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This work applied remote sensing techniques based on unmanned aerial vehicles (UAVs) and deep learning (DL) to detect WLD in sugarcane fields at the Gal-Oya Plantation, Sri Lanka. The established
methodology to detect WLD consists of UAV red, green, and blue (RGB) image acquisition, the
pre-processing of the dataset, labelling, DL model tuning, and prediction.


  • Narmilan Amarasingam conducted the UAV flight mission, and analysis and prepared the manuscript for final submission as a corresponding author.
  • Felipe Gonzalez, Kevin Powell, and Juan Sandino 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 provided feedback on the draft manuscript.

Geographical area of data collection



Narmilan, Amarasingam, Gonzalez, Felipe, Salgadoe, Arachchige Surantha Ashan, & Powell, Kevin (2022) Detection of White Leaf Disease in Sugarcane Using Machine Learning Techniques over UAV Multispectral Images. Drones, 6(9), Article number: 230. https://eprints.qut.edu.au/235559/

Research areas

Precision agriculture
Object detection
Convolutional neural networks
White leaf disease
Remote sensing
Machine learning

Cite this collection

Narmilan, Amarasingam; (2022): Image dataset for detecting sugarcane white leaf disease using Deep learning. Queensland University of Technology. (Dataset) https://doi.org/10.25912/RDF_1670808596168

Partner institution

Gal-Oya Plantations (Pvt) Limited https://www.galoya.lk/

Data file types

UAV raw images (.tiff)


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


© Narmilan Amarasingam, 2022.

Dates of data collection

From 2021-10-13 to 2021-10-13


Has association with
Felipe Gonzalez  (Researcher)
Juan Sandino  (Researcher)


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