Mapping and Estimating Weeds in Cotton Using Unmanned Aerial Systems-Borne Imagery
| dc.contributor.author | Sapkota, Bishwa | en |
| dc.contributor.author | Singh, Vijay | en |
| dc.contributor.author | Cope, Dale | en |
| dc.contributor.author | Valasek, John | en |
| dc.contributor.author | Bagavathiannan, Muthukumar V. | en |
| dc.contributor.department | Virginia Agricultural Experiment Station | en |
| dc.date.accessioned | 2020-06-30T16:38:21Z | en |
| dc.date.available | 2020-06-30T16:38:21Z | en |
| dc.date.issued | 2020-06-16 | en |
| dc.date.updated | 2020-06-30T16:27:49Z | en |
| dc.description.abstract | In recent years, Unmanned Aerial Systems (UAS) have emerged as an innovative technology to provide spatio-temporal information about weed species in crop fields. Such information is a critical input for any site-specific weed management program. A multi-rotor UAS (Phantom 4) equipped with an RGB sensor was used to collect imagery in three bands (Red, Green, and Blue; 0.8 cm/pixel resolution) with the objectives of (a) mapping weeds in cotton and (b) determining the relationship between image-based weed coverage and ground-based weed densities. For weed mapping, three different weed density levels (high, medium, and low) were established for a mix of different weed species, with three replications. To determine weed densities through ground truthing, five quadrats (1 m × 1 m) were laid out in each plot. The aerial imageries were preprocessed and subjected to Hough transformation to delineate cotton rows. Following the separation of inter-row vegetation from crop rows, a multi-level classification coupled with machine learning algorithms were used to distinguish intra-row weeds from cotton. Overall, accuracy levels of 89.16%, 85.83%, and 83.33% and kappa values of 0.84, 0.79, and 0.75 were achieved for detecting weed occurrence in high, medium, and low density plots, respectively. Further, ground-truthing based overall weed density values were fairly correlated (r<sup>2</sup> = 0.80) with image-based weed coverage assessments. Among the specific weed species evaluated, Palmer amaranth (<i>Amaranthus palmeri </i>S. Watson) showed the highest correlation (r<sup>2</sup> = 0.91) followed by red sprangletop (<i>Leptochloa mucronata</i> Michx) (r<sup>2</sup> = 0.88). The results highlight the utility of UAS-borne RGB imagery for weed mapping and density estimation in cotton for precision weed management. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Sapkota, B.; Singh, V.; Cope, D.; Valasek, J.; Bagavathiannan, M. Mapping and Estimating Weeds in Cotton Using Unmanned Aerial Systems-Borne Imagery. AgriEngineering 2020, 2, 350-366. | en |
| dc.identifier.doi | https://doi.org/10.3390/agriengineering2020024 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/99193 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | digital agronomy | en |
| dc.subject | Hough transformation | en |
| dc.subject | Machine learning | en |
| dc.subject | object-based image analysis | en |
| dc.subject | precision agriculture | en |
| dc.title | Mapping and Estimating Weeds in Cotton Using Unmanned Aerial Systems-Borne Imagery | en |
| dc.title.serial | AgriEngineering | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |
| dc.type.dcmitype | StillImage | en |