2018
DOI: 10.1016/j.compag.2018.03.026
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Plant discrimination by Support Vector Machine classifier based on spectral reflectance

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Cited by 75 publications
(37 citation statements)
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“…More recently, remote sensing has been used to distinguish weeds relative to cash crops, such as corn, cabbage ( Brassica olearacea var. capitata ), sugar beet ( Beta vulgaris L.), and soybean . Several studies have provided insights for developing sprayers based on the SSWM approach, with limitations mainly related to identifying weeds and other targets with different spatial resolutions …”
Section: Discussionmentioning
confidence: 99%
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“…More recently, remote sensing has been used to distinguish weeds relative to cash crops, such as corn, cabbage ( Brassica olearacea var. capitata ), sugar beet ( Beta vulgaris L.), and soybean . Several studies have provided insights for developing sprayers based on the SSWM approach, with limitations mainly related to identifying weeds and other targets with different spatial resolutions …”
Section: Discussionmentioning
confidence: 99%
“…capitata), sugar beet (Beta vulgaris L.), and soybean. 15,18,27,32,55,56 Several studies have provided insights for developing sprayers based on the SSWM approach, with limitations mainly related to identifying weeds and other targets with different spatial resolutions. 6,72,73 Considering the scientific literature on spectral information for different targets before planting, an important outcome of this study was related to the similarity of spectral curves between training (field trial) and validation (on-farm) data sets to assess weeds, soil texture, and crop residues.…”
Section: Discussionmentioning
confidence: 99%
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“…Furthermore, spectral reflectance measurements are used for discriminating between crops and weeds [14] and [15]. In [14] an SVM along with spectral reflectance measurements are combined for developing a corn/silverbeet (as crop-weed) differentiation system. The intensities of the reflectance of laser beams off soil and vegetation at three wavelengths are gathered by a weed sensor.…”
Section: Related Workmentioning
confidence: 99%