A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance

Precision agriculture relies heavily on accurate, efficient, and economical methods to distinguish between crops and weeds of various types. The advancement of unmanned aerial vehicle (UAV) technologies provides practical approaches for generating land-cover maps that are essential for monitoring and managing crop fields affected by various weeds. Although the overall cost of scouting crop fields with UAVs may be low and practical, it varies depending on the sensors used; and the existing studies have mainly focused on weed detection methods but not compared the sensors’ performance. Therefore, to address this knowledge gap, this research aims to compare a UAV-mounted visual Red-Green-Blue (RGB) sensor and a multispectral sensor in differentiating between crops and weeds in soybean fields, with a particular focus on broadleaf and grass weeds. In this research, a field study was conducted using a support vector machine classification algorithm and object-based image analysis in ArcGIS Pro to examine the impact of sensor choice on weed type differentiation. The analysis with ground truths highlights nuanced discrepancies between the sensors, namely (i) DJI Phantom 4 Pro (RGBd), and (ii) DJI Phantom 4 Multispectral. Overall, with the RGB sensor, an accuracy of 93.8% was achieved in identifying the land cover types, and the multispectral sensor also had an accuracy of 93.4% in discriminating the various land cover types. These results show that both sensor's performances were similar, but the less expensive RGB sensor may be better suited precision agriculture at all scales. • The rise of herbicide-resistant weeds has made effective weed control more challenging. • Selective herbicides are effective in controlling specific types of weeds. • Drone-mounted RGB and multispectral sensors can enhance large-scale weed-type identification. • Multispectral sensors offer superior spectral bands compared to RGB sensors. • Drones equipped with RGB sensors are more affordable than those with multispectral sensors.

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Publication Details

Journal
Journal of Agriculture and Food Research
Published
2025-03-06
DOI
https://doi.org/10.1016/j.jafr.2025.101784
Citations
4
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
3.22

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article

A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance

Joseph Mettler, Cannayen Igathinathane, Kelvin Betitame, Cengiz Koparan et al.
4 citations
Journal of Agriculture and Food Research
Identification and Quantification in Food
3.22
article

A practical guide to UAV-based weed identification in soybean: Comparing RGB and multispectral sensor performance

Joseph Mettler, Cannayen Igathinathane, Kelvin Betitame, Cengiz Koparan, Xin Sun, Kirk Howatt
article en
4 citations

Abstract

Precision agriculture relies heavily on accurate, efficient, and economical methods to distinguish between crops and weeds of various types. The advancement of unmanned aerial vehicle (UAV) technologies provides practical approaches for generating land-cover maps that are essential for monitoring and managing crop fields affected by various weeds. Although the overall cost of scouting crop fields with UAVs may be low and practical, it varies depending on the sensors used; and the existing studies have mainly focused on weed detection methods but not compared the sensors’ performance. Therefore, to address this knowledge gap, this research aims to compare a UAV-mounted visual Red-Green-Blue (RGB) sensor and a multispectral sensor in differentiating between crops and weeds in soybean fields, with a particular focus on broadleaf and grass weeds. In this research, a field study was conducted using a support vector machine classification algorithm and object-based image analysis in ArcGIS Pro to examine the impact of sensor choice on weed type differentiation. The analysis with ground truths highlights nuanced discrepancies between the sensors, namely (i) DJI Phantom 4 Pro (RGBd), and (ii) DJI Phantom 4 Multispectral. Overall, with the RGB sensor, an accuracy of 93.8% was achieved in identifying the land cover types, and the multispectral sensor also had an accuracy of 93.4% in discriminating the various land cover types. These results show that both sensor's performances were similar, but the less expensive RGB sensor may be better suited precision agriculture at all scales. • The rise of herbicide-resistant weeds has made effective weed control more challenging. • Selective herbicides are effective in controlling specific types of weeds. • Drone-mounted RGB and multispectral sensors can enhance large-scale weed-type identification. • Multispectral sensors offer superior spectral bands compared to RGB sensors. • Drones equipped with RGB sensors are more affordable than those with multispectral sensors.

Journal of Agriculture and Food ResearchVol. 20
North Dakota State University (US), University of Arkansas at Fayetteville (US)
U.S. Department of Agriculture, National Institute of Food and Agriculture
Zero hunger
Openalex Percentile: Top 9%
Identification and Quantification in Food
3.22
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