|ORTIZ, BRENDA - Auburn University|
|DING, WEI - Northeast Agricultural University|
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/10/2011
Publication Date: 6/12/2011
Citation: Ortiz, B.V., Thomson, S.J., Huang, Y., Reddy, K.N., Ding, W. 2011. Determination of differences in crop injury from aerial application of glyphosate using vegetation indices. Computers and Electronics in Agriculture. 77:204-213.
Interpretive Summary: Crop injury caused by off-target drift of glyphosate, one of the most common non-selective herbicides used in row crop production, can seriously reduce growth and yield. This is of great concern to farmers and aerial applicators. A method to remotely sense levels and extent of crop injury could be effective to support management decisions and improve drift control measures. A study was conducted to evaluate several mathematical methods (or vegetation indices) on images obtained from remote sensing to identify glyphosate injury to corn, cotton, and soybean. Geostatistical methods, which can estimate spatial data where little or no information is available, were used with these vegetation indices. Results suggested that one vegetation index (Chlorophyll Vegetation Index or CVI) was best at identifying glyphosate injury, and analysis of geostatistics indicated the extent of crop damage.
Technical Abstract: Crop injury caused by off-target drift of herbicide can seriously reduce growth and yield and is of great concern to farmers and aerial applicators. Farmers can benefit from identifying an indirect method for assessing the levels and extent of crop injury. This study evaluates the combined use of geostatistical methods and vegetation indices (VIs) derived from multispectral images to assess the level and extent of crop injury. An experiment was conducted in 2009 to determine glyphosate injury differences among the cotton, corn, and soybean crops. The crops were planted in eight row strips spaced 102cm apart and 80 m long with four replications. Seven VIs were calculated from multispectral images collected at 7 and 21 days after the glyphosate application (DAA). At each image collection date, visual injury estimates were assessed and data were collected for plant height, chlorophyll content, and shoot dry weight. From the seven VIs evaluated as surrogate for glyphosate injury identification using a canonical correlation analysis (CCA), the Chlorophyll Vegetation Index (CVI) showed the highest correlation with field-measured plant injury data. Differences in the level of injury by crop were assessed by observing residual values calculated from the CVI images. Areas of severe to moderate injury were identified where soybean exhibited greater injury than corn or cotton. The extent of injury, calculated as the range of spatial correlation from the CVI residual images, was higher for corn than cotton and soybean. When comparing the ranges calculated from the 7DAA and 21DAA CVI residual images, it was possible to determine an increase in the extent of damage, especially for corn, throughout the growing season. The techniques evaluated in this study seem promising for estimating the level and extent and glyphosate herbicide drift which might result in appropriate and timely management decisions.