Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic objec...

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Bibliographic Details
Published in:PloS one 2013-10, Vol.8 (10), p.e77151-e77151
Main Authors: Peña, José Manuel, Torres-Sánchez, Jorge, de Castro, Ana Isabel, Kelly, Maggi, López-Granados, Francisca
Format: Article
Language:English
Subjects:
Agricultural economics
Agriculture
Aircraft
Analysis
Avena sterilis
Classification
Corn
Councils
Crops
Data processing
Global positioning systems
GPS
Herbicides
I.R. radiation
Identification
Image analysis
Image processing
Infestation
Infrared cameras
Novellas
Plant Weeds
Remote Sensing Technology - instrumentation
Remote Sensing Technology - methods
Remotely piloted vehicles
Satellite imagery
Seasons
Statistical analysis
Sustainable agriculture
Unmanned aerial vehicles
Weed control
Weed Control - methods
Weeds
Zea mays - growth & development
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Summary:The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r(2)=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0077151