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@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 nov. 2020"
}


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