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United States Department of Agriculture

Agricultural Research Service


item Henry, William
item Shaw, David
item Reddy, Kambham
item Bruce, Lori
item Tamhankar, Hrishikesh

Submitted to: Weed Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/30/2004
Publication Date: 4/1/2004
Citation: Henry, W.B., Shaw, D.R., Reddy, K.R., Bruce, L.M., Tamhankar, H.D. 2004. Remote sensing to detect herbicide drift on crops. Weed Technology. 18:358-368.

Interpretive Summary: The use of non-selective herbicides like glyphosate and paraquat has increased as producers decrease tillage and depend more on chemical weed control. Crops that have been genetically modified to withstand applications of these non-selective compounds have also increased in frequency. As the use of these compounds has increased, so has the amount of complaints regarding unwanted drift events. This experiment was designed to measure how well remotely sensed data could be used to detect and assess drift damage on crops. One of the most promising techniques for detecting paraquat drift on corn was able to correctly identify even slight injury caused by low rates of herbicide (compared to plants that were not sprayed with paraquat) 90% of the time at 1, 4, and 7 days after herbicide application. These analysis techniques are ready to be applied to field scale data.

Technical Abstract: Glyphosate and paraquat herbicide drift injury to crops may substantially reduce growth or yield. Determining the type and degree of injury is of importance to a producer. This research was conducted to determine if remote sensing could be used to identify and quantify herbicide injury to crops. Soybean and corn plants were grown in were grown in 3.8-L pots to the five to seven leaf stage, at which time applications of non-selective herbicides were made. Visual injury estimates were made and hypersepctral reflectance data were recorded at 1, 4, and 7 days after application (DAA). Several analysis techniques including multiple indices, signature amplitude with spectral bands as features, and wavelet analysis were employed to distinguish between herbicide-treated and nontreated plants. Classification accuracy using signature amplitude (SA) analysis of paraquat injury on soybean was better than 75% for both 1/2 and 1/8X rates at 1, 4, and 7 DAA. Classification accuracy of paraquat injury on corn was better than 72% for the 1/2X rate at 1, 4, and 7 DAA. These data suggest that hyperspectral reflectance may be used to distinguish between healthy plants and injured plants to which herbicides have been applied; however, the classification accuracies remained at 75% or higher only when the higher rates of herbicide were applied. Applications of a 1/2X rate of glyphosate produced 55 to 81% soybean injury and 20 to 50% corn injury 4 and 7 DAA, respectively. However, using signature amplitude analysis, the moderately-injured plants were indistinguishable from the uninjured controls, as represented by the low classification accuracies at the 1/8, 1/32, and 1/64X rates. The most promising technique for identifying drift injury was wavelet analysis, which successfully distinguished between corn plants treated with either the eighth or the half-X rates of paraquat compared to the nontreated corn plants better than 92% at 1, 4, and 7 DAA. These analysis techniques, once tested and validated on field scale data, may help determine the extent and the degree of herbicide drift for making appropriate and more importantly timely management decisions.

Last Modified: 06/24/2017
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