Next Article in Journal
Quadcopter-Based Rapid Response First-Aid Unit with Live Video Monitoring
Next Article in Special Issue
A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses
Previous Article in Journal
Drone Laser Scanning for Modeling Riverscape Topography and Vegetation: Comparison with Traditional Aerial Lidar
Previous Article in Special Issue
Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery
Article Menu
Issue 2 (June) cover image

Export Article

Open AccessArticle

Assessment of Texture Features for Bermudagrass (Cynodon dactylon) Detection in Sugarcane Plantations

National Institute for Space Research–Remote Sensing Division, São José dos Campos, São Paulo 12227-010, Brazil
National Institute for Space Research–Image Processing Division, São José dos Campos, São Paulo 12227-010, Brazil
Author to whom correspondence should be addressed.
Drones 2019, 3(2), 36;
Received: 27 February 2019 / Revised: 5 April 2019 / Accepted: 10 April 2019 / Published: 13 April 2019
(This article belongs to the Special Issue UAV/Drones for Agriculture and Forestry)
PDF [24026 KB, uploaded 13 April 2019]


Sugarcane products contribute significantly to the Brazilian economy, generating U.S. $12.2 billion in revenue in 2018. Identifying and monitoring factors that induce yield reduction, such as weed occurrence, is thus imperative. The detection of Bermudagrass in sugarcane crops using remote sensing data, however, is a challenge considering their spectral similarity. To overcome this limitation, this paper aims to explore the potential of texture features derived from images acquired by an optical sensor onboard anunmanned aerial vehicle (UAV) to detect Bermudagrass in sugarcane. Aerial images with a spatial resolution of 2 cm were acquired from a sugarcane field in Brazil. The Green-Red Vegetation Index and several texture metrics derived from the gray-level co-occurrence matrix were calculated to perform an automatic classification using arandom forest algorithm. Adding texture metrics to the classification process improved the overall accuracy from 83.00% to 92.54%, and this improvement was greater considering larger window sizes, since they representeda texture transition between two targets. Production losses induced by Bermudagrass presence reached 12.1 tons × ha−1 in the study site. This study not only demonstrated the capacity of UAV images to overcome the well-known limitation of detecting Bermudagrass in sugarcane crops, but also highlighted the importance of texture for high-accuracy quantification of weed invasion in sugarcane crops. View Full-Text
Keywords: remote sensing; classification; agriculture; weed detection; UAV images remote sensing; classification; agriculture; weed detection; UAV images

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Girolamo-Neto, C.D.; Sanches, I.D.; Neves, A.K.; Prudente, V.H.R.; Körting, T.S.; Picoli, M.C.A.; Aragão, L.E.O.C. Assessment of Texture Features for Bermudagrass (Cynodon dactylon) Detection in Sugarcane Plantations. Drones 2019, 3, 36.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics



[Return to top]
Drones EISSN 2504-446X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top