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United States Department of Agriculture

Agricultural Research Service

Research Project: Pesticide Application Technologies for Spray-drift Management, Maximizing In-field Deposition, and Targeted Spraying

Location: Crop Production Systems Research Unit

Title: Early detection of crop injury from herbicide glyphosate by leaf biochemical parameter inversion

Authors
item Zhao, Feng -
item Guo, Yiqing -
item Huang, Yanbo
item Reddy, Krishna
item Lee, Matthew
item Fletcher, Reginald
item Thomson, Steven
item Zhao, Huijie -

Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 15, 2014
Publication Date: April 2, 2014
Citation: Zhao, F., Guo, Y., Huang, Y., Reddy, K.N., Lee, M.A., Fletcher, R.S., Thomson, S.J., Zhao, H. 2014. Early detection of crop injury from herbicide glyphosate by leaf biochemical parameter inversion. International Journal of Applied Earth Observation and Geoinformation. 31:78-85.

Interpretive Summary: Although plant leaf optical spectrum could provide useful information for early detection of crop injury from off-target glyphosate drift, conventional remote sensing data processing and analysis methods cannot generate satisfactory results for the detection. The scientists at the School of Instrumentation Science and Opto-Electronics Engineering, Beihang University in Beijing, China and USDA-ARS Crop production Systems Research Unit, Stoneville, MS worked collaboratively to study a new method for inversion of plant leaf biochemical inversion using a physically based radiation transfer model. With leaf hyperspectral reflectance measurements from non-glyphosate-resistant (non-GR) soybean and non-GR cotton leaves as the model outputs the model inverts the inputs, leaf chlorophyll content, equivalent water thickness, and leaf mass per area. The results indicated that the parameter inversions were satisfactory with reasonably high coefficient of determination and reasonably low root-mean-square error for both of soybean and cotton. Further it was found that the crop leaf injury caused by glyphosate could be detected shortly after the spraying for both soybean and cotton using the method of parameter inversion. These findings show the potential of applying the method of leaf biochemical parameter inversion for the early detection of leaf injury from off-target drift of glyphosate.

Technical Abstract: Early detection of crop injury from glyphosate is of significant importance in crop management. In this paper, we attempt to detect glyphosate-induced crop injury by PROSPECT (leaf optical PROperty SPECTra model) inversion through leaf hyperspectral reflectance measurements for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton leaves. The PROSPECT model was calibrated to retrieve chlorophyll content (Ca+b), equivalent water thickness (Cw), and leaf mass per area (Cm) from leaf hyperspectral reflectance spectra. The leaf stress conditions were then evaluated by examining the temporal variations of these biochemical constituents after glyphosate treatment. The approach was validated with greenhouse measured datasets. Results indicated that the coefficient of determination (R2) of Ca+b, Cw, and Cm were greater than 0.8, 0.7, and 0.5, respectively, for both soybean and cotton. The Root-Mean-Square Error (RMSE) values of Ca+b, Cw, and Cm were reasonably low with 1.2278 µg/cm2, 0.0005 g/cm2, and 0.0042 g/cm2 for soybean and 0.9144 µg/cm2, 0.0124 g/cm2, and 0.0003 g/cm2 for cotton, respectively. It was further found that the leaf injury caused by glyphosate treatments could be detected shortly after the spraying for both soybean and cotton by PROSPECT inversion, with Ca+b of the leaves treated with high dose solution decreasing more rapidly compared with leaves left untreated, whereas the Cw and Cm showed no obvious difference between treated and untreated leaves. These findings demonstrate the feasibility of applying the PROSPECT inversion technique for the early detection of leaf injury from glyphosate and its potential for agricultural plant status monitoring.

Last Modified: 11/23/2014
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