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

Peña, José Manuel ; Torres-Sánchez, Jorge ; de Castro, Ana Isabel ; Kelly, Maggi ; López-Granados, Francisca Suarez, Oscar Deniz

PloS one, 2013-10, Vol.8 (10), p.e77151-e77151 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images
Autor: Peña, José Manuel ; Torres-Sánchez, Jorge ; de Castro, Ana Isabel ; Kelly, Maggi ; López-Granados, Francisca
Suarez, Oscar Deniz
Assuntos: Agricultural economics ; Agriculture ; Aircraft ; Analysis ; Avena sterilis ; Classification ; Corn ; Councils ; Crops ; Data processing ; Drone aircraft ; 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 ; Vehicles, Remotely piloted ; Weed control ; Weed Control - methods ; Weeds ; Zea mays - growth & development
É parte de: PloS one, 2013-10, Vol.8 (10), p.e77151-e77151
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: JMP MK FLG. Performed the experiments: JMP AIdC. Analyzed the data: JMP JTS. Contributed reagents/materials/analysis tools: JMP MK FLG. Wrote the manuscript: JMP MK FLG.
Descrição: 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 (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.
Editor: United States: Public Library of Science
Idioma: Inglês

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

Peña, José Manuel ; Torres-Sánchez, Jorge ; de Castro, Ana Isabel ; Kelly, Maggi ; López-Granados, Francisca Suarez, Oscar Deniz

United States: Public Library of Science

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