|YAO, HAIBO - Mississippi State University|
|HRUSKA, ZUZANA - Mississippi State University|
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/2/2012
Publication Date: 10/8/2012
Citation: Yao, H., Huang, Y., Hruska, Z., Thomson, S.J., Reddy, K.N. 2012. Using vegetative index and modified derivative for early detection of soybean plant injury from glyphosate. Computers and Electronics in Agriculture. 89:145-157.
Interpretive Summary: Early detection of crop injury caused from off-target drift of herbicide application is critical in crop production management. Detection of subtle changes in the reduction in chlorophyll content and metabolic disturbances provide an optical approach to studying the onset of crop injury caused by herbicide. Scientists from Geosystems Research Institute, Mississippi State University and USDA-ARS, Crop Production Systems Research Units have conducted a greenhouse study to measure the canopy reflectance of soybean plants using a portable hyperspectral image sensor. The results of data analysis indicated that the vegetation indices derived from the hyperspectral images could identify crop injury at 24 hours after application before visualization of glyphosate injured from the non-treated plants. To improve the results the method was further improved by modifying the standard spectral derivative analysis, and the finding was that the modified method produced significantly better results than the vegetation indices. This method could potentially differentiate crop injury at 4 hours after treatment. This study showed that measuring plant canopy reflectance via hyperspectral imaging could provide useful information for early detection of soybean crop injury from glyphosate.
Technical Abstract: Glyphosate is a non-selective, systemic herbicide highly toxic to sensitive plant species, and its use has seen a significant increase due to the increased adoption of genetically modified glyphosate-resistant crops since the mid-1990s. Glyphosate application for weed control in glyphosate-resistant crops can drift onto an off-target area, causing unwanted injury to non-glyphosate resistant plants. Thus, early detection of crop injury from off-target drift of herbicide is critical in crop production. In non-glyphosate- resistant plants, glyphosate causes a reduction in chlorophyll content and metabolic disturbances. These subtle changes may be detectable by plant reflectance, which suggests the possibility of using optical remote sensing for early detection of drift damage to plants. In order to determine the feasibility of using optical remote sensing, a greenhouse study was initiated to measure the canopy reflectance of soybean plants using a portable hyperspectral image sensor. Non-glyphosate resistant soybean (Glycine max L. Merr.) plants were treated with glyphosate using a pneumatic track sprayer in a spray chamber. The three treatment groups were control (0 kg ae/ha), low dosage (0.086 kg ae/ha), and high dosage (0.86 kg ae/ha), each with four 2-plant pots. Hyperspectral images were taken at 4, 24, 48, and 72 hours after application. The extracted canopy reflectance data was analyzed with vegetation indices. The results indicated that a number of vegetation indices could identify crop injury at 24 hours after application, at which time visual inspection could not distinguish between glyphosate injured and non-treated plants. To improve the results a modified method of spectral derivative analysis was proposed and applied to find that the method produced significantly better results than the vegetation indices. Four selected first derivatives at wavelength 519 nm, 670, 685, and 697 nm could potentially differentiate crop injury at 4 hours after treatment. The overall false positive rate was lower than the vegetation indices. Furthermore, the derivatives demonstrated the ability to separate treatment groups with different dosages. The study showed that hyperspectral imaging of plant canopy reflectance could be a useful tool for early detection of soybean crop injury from glyphosate, and the modified spectral derivative analysis had a significantly better performance than vegetation indices.