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Weed species differentiation using spectral reflectance land image classification
Author(s): J. T. Sanders; W. J. Everman; R. Austin; G. T. Roberson; R. J. Richardson
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Paper Abstract

Advancements in efficient unmanned aerial platforms and affordable sensors has led to renewed interest in remote sensing by agricultural producers and land managers for use as an efficient and convenient method of evaluating crop status and pest issues in their fields. For remote sensing to be employed as a viable and widespread tool for weed management, the accurate detection of distinct weed species must be possible through the use of analytical procedures on the resultant imagery. Additionally, the remote sensing platform and subsequent analysis must be capable of identifying these species across a wide range of heights. In 2017, a field study was performed to identify any weed height thresholds on the accurate detection and subsequent classification of three common broadleaf weed species in the southeastern United States: Palmer amaranth (Amaranthus palmeri), common ragweed (Ambrosia artemisiifolia) and sicklepod Senna obtusifolia) as well as the classification accuracy of image classifications performed on the species scale. Pots of the three species at heights of 5, 10, 15, and 30 cm were randomly arranged in a grid and 5-band multispectral imagery was collected at 15 m. Image analysis was used to identify the spectral reflectance behavior of the weed species and height combinations and to evaluate the accuracy of species based supervised classifications involving the three species. Supervised classification was able to discriminate between the three weed species with between 24-100% accuracy depending on height and species. Palmer amaranth classification accuracy was consistently 100%. Increased height of sicklepod and common ragweed plants did not reliably confer improved accuracy but the species were correctly identified with at least 24% and 60% accuracy, respectively.

Paper Details

Date Published: 10 May 2019
PDF: 9 pages
Proc. SPIE 11007, Advanced Environmental, Chemical, and Biological Sensing Technologies XV, 110070P (10 May 2019); doi: 10.1117/12.2519306
Show Author Affiliations
J. T. Sanders, North Carolina State Univ. (United States)
W. J. Everman, North Carolina State Univ. (United States)
R. Austin, North Carolina State Univ. (United States)
G. T. Roberson, North Carolina State Univ. (United States)
R. J. Richardson, North Carolina State Univ. (United States)


Published in SPIE Proceedings Vol. 11007:
Advanced Environmental, Chemical, and Biological Sensing Technologies XV
Tuan Vo-Dinh, Editor(s)

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