@Article{Girolamo-NetoSaNePrKöPiAr:2019:AsTeFe,
author = "Girolamo-Neto, Cesare Di and Sanches, Ieda Del'Arco and Neves,
Alana Kasahara and Prudente, Victor Hugo Rohden and K{\"o}rting,
Thales Sehn and Picoli, Michelle Cristina Ara{\'u}jo and
Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Assessment of Texture Features for Bermudagrass (Cynodon dactylon)
Detection in Sugarcane Plantations",
journal = "Drones",
year = "2019",
volume = "3",
number = "2",
pages = "36",
abstract = "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 consideringtheir 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 2cm 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.",
doi = "10.3390/drones3020036",
url = "http://dx.doi.org/10.3390/drones3020036",
issn = "2504-446X",
label = "lattes: 2456184661855977 2
Girolamo-NetoSaNePrK{\"o}PiAr:2019:AsTeFe",
language = "en",
targetfile = "drones-03-00036.pdf",
urlaccessdate = "28 abr. 2024"
}