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Proceedings Paper

Differentiating glyphosate-resistant and glyphosate-sensitive Italian ryegrass using hyperspectral imagery
Author(s): Matthew A. Lee; Yanbo Huang; Vijay K. Nandula; Krishna N. Reddy
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Paper Abstract

Glyphosate based herbicide programs are most preferred in current row crop weed control practices. With the increased use of glyphosate, weeds, including Italian ryegrass (Lolium multiflorum), have developed resistance to glyphosate. The identification of glyphosate resistant weeds in crop fields is critical because they must be controlled before they reduce the crop yield. Conventionally, the method for the identification with whole plant or leaf segment/disc shikimate assays is tedious and labor-intensive. In this research, we investigated the use of high spatial resolution hyperspectral imagery to extract spectral curves derived from the whole plant of Italian ryegrass to determine if the plant is glyphosate resistant (GR) or glyphosate sensitive (GS), which provides a way for rapid, non-contact measurement for differentiation between GR and GS weeds for effective site-specific weed management. The data set consists of 226 greenhouse grown plants (119 GR, 107 GS), which were imaged at three and four weeks after emergence. In image preprocessing, the spectral curves are normalized to remove lighting artifacts caused by height variation in the plants. In image analysis, a subset of hyperspectral bands is chosen using a forward selection algorithm to optimize the area under the receiver operating characteristic (ROC) between GR and GS plants. Then, the dimensionality of selected bands is reduced using linear discriminant analysis (LDA). Finally, the maximum likelihood classification was conducted for plant sample differentiation. The results show that the overall classification accuracy is between 75% and 80% depending on the age of the plants. Further refinement of the described methodology is needed to correlate better with plant age.

Paper Details

Date Published: 28 May 2014
PDF: 7 pages
Proc. SPIE 9108, Sensing for Agriculture and Food Quality and Safety VI, 91080B (28 May 2014); doi: 10.1117/12.2053072
Show Author Affiliations
Matthew A. Lee, Mississippi State Univ. (United States)
Yanbo Huang, USDA Agricultural Research Service (United States)
Vijay K. Nandula, USDA Agricultural Research Service (United States)
Krishna N. Reddy, USDA Agricultural Research Service (United States)

Published in SPIE Proceedings Vol. 9108:
Sensing for Agriculture and Food Quality and Safety VI
Moon S. Kim; Kuanglin Chao, Editor(s)

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