2 results
Remote Sensing to Detect Herbicide Drift on Crops
- W. Brien Henry, David R. Shaw, Kambham R. Reddy, Lori M. Bruce, Hrishikesh D. Tamhankar
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- Journal:
- Weed Technology / Volume 18 / Issue 2 / June 2004
- Published online by Cambridge University Press:
- 20 January 2017, pp. 358-368
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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 whether remote sensing could be used to identify and quantify herbicide injury to crops. Soybean and corn plants were grown in 3.8-L pots to the five- to seven-leaf stage, at which time, applications of nonselective herbicides were made. Visual injury estimates were made, and hyperspectral reflectance data were recorded 1, 4, and 7 d after application (DAA). Several analysis techniques including multiple indices, signature amplitude (SA) with spectral bands as features, and wavelet analysis were used to distinguish between herbicide-treated and nontreated plants. Classification accuracy using SA analysis of paraquat injury on soybean was better than 75% for both 1/2- and 1/8× rates at 1, 4, and 7 DAA. Classification accuracy of paraquat injury on corn was better than 72% for the 1/2× 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 1/2× rate of glyphosate produced 55 to 81% soybean injury and 20 to 50% corn injury 4 and 7 DAA, respectively. However, using SA 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 1/64× rates. The most promising technique for identifying drift injury was wavelet analysis, which successfully distinguished between corn plants treated with either the 1/8- or the 1/2× rates of paraquat compared with the nontreated corn plants better than 92% 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.
Remote Sensing to Distinguish Soybean from Weeds After Herbicide Application
- W. Brien Henry, David R. Shaw, Kambham R. Reddy, Lori M. Bruce, Hrishikesh D. Tamhankar
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- Journal:
- Weed Technology / Volume 18 / Issue 3 / September 2004
- Published online by Cambridge University Press:
- 20 January 2017, pp. 594-604
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Two experiments, one focusing on preemergence (PRE) herbicides and the other on postemergence (POST) herbicides, were conducted and repeated in time to examine the utility of hyperspectral remote sensing data for discriminating common cocklebur, hemp sesbania, pitted morningglory, sicklepod, and soybean after PRE and POST herbicide application. Discriminant models were created from combinations of multiple indices. The model created from the second experimental run's data set and validated on the first experimental run's data provided an average of 97% correct classification of soybean and an overall average classification accuracy of 65% for all species. These data suggest that these models are relatively robust and could potentially be used across a wide range of herbicide applications in field scenarios. From the data set pooled across time and experiment types, a single discriminant model was created with multiple indices that discriminated soybean from weeds 88%, on average, regardless of herbicide, rate, or species. Signature amplitudes, an additional classification technique, produced variable results with respect to discriminating soybean from weeds after herbicide application and discriminating between controls and plants to which herbicides were applied; thus, this was not an adequate classification technique.