Remote Sensing to Detect Herbicide Drift on Crops

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 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.

Assessing crop damage from dicamba on non‐dicamba‐tolerant soybean by hyperspectral imaging through machine learning

“…As for detecting herbicide injury in crops, Henry et al . used hyperspectral data to differentiate soybean and corn plants that are treated with glyphosate and paraquate for the untreated ones.…”

Assessment of soybean injury from glyphosate using airborne multispectral remote sensing

“…Henry et al . indicated that multiple indices formulated from the band information extracted from hyperspectral reflectance could distinguish between healthy and injured soybean and corn plants to which herbicides, glyphosate and paraquat had been applied.…”

Early Detection of Soybean Plant Injury from Glyphosate by Measuring Chlorophyll Reflectance and Fluorescence

“…Remote sensing methods have been developed in an attempt to detect crop injury more effectively. The methods include multispectral imaging (Thelen et al, 2004;Huang et al, 2010) and spectral reflectance measurement (Henry et al, 2004). Moreover, it would be of interest to know if measurement of narrow-banded reflectance has the capability of revealing subtle changes in canopy reflectance, which could present more useful information in detecting the onset of stress from crop injury.…”