Abstract
In this paper, we aim to detect crop injury from glyphosate, a herbicide, by both traditionally used spectral indices and newly extracted features with leaf hyperspectral reflectance data for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton. The new features were extracted by canonical analysis technique, which could provide the largest separability to distinguish the injured leaves from the healthy ones. Spectral bands used for constructing these new features were selected based on the sensitivity analysis results of a physically-based leaf radiation transfer model (leaf optical PROperty SPECTra model, PROSPECT), which could help extend the effectiveness of these features to a wide range of leaf structures and growing conditions. This approach has been validated with greenhouse measured data acquired in glyphosate treatment experiments. Results indicated that glyphosate injury could be detected by NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and DVI (Difference Vegetation Index) in 48 h After the Treatment (HAT) for soybean and in 72 HAT for cotton, but the other spectral indices either showed little use for separation, or did not show consistent separation for healthy and injured soybean and cotton. Compared with the traditional spectral indices, the new features were more feasible for the early detection of glyphosate injury, with leaves sprayed with a higher rate of glyphosate solution having larger feature values. This trend became more and more pronounced with time. Leaves sprayed with different glyphosate rates showed some separability 24 HAT using the new features and could be totally distinguished at and beyond 48 HAT for both soybean and cotton. These findings demonstrated the feasibility of applying leaf hyperspectral reflectance measurements for the early detection of glyphosate injury using these newly proposed features.
Highlights
With the increased use of glyphosate as an herbicide in Glyphosate-Resistant (GR) cropping systems, glyphosate drift has been of particular concern in recent years [1]
Results showed that means of Chl were significantly different between each group at and beyond 48 h After the Treatment (HAT) for both soybean and cotton, whereas the Equivalent Water Thickness (EWT) and Leaf Mass per Area (LMA) showed no significant difference during 6–72 HAT
This study demonstrated that by using traditional spectral indices and newly extracted first canonical axis (FCA) features (FCAs for soybean and FCAc for cotton), non-GR crop injury caused by glyphosate could be detected shortly after the spray by plant leaf reflectance spectra
Summary
With the increased use of glyphosate as an herbicide in Glyphosate-Resistant (GR) cropping systems, glyphosate drift has been of particular concern in recent years [1]. About 30 cases of herbicide drift onto non-target crops are reported in Mississippi each year, with more than 70% of these caused by glyphosate [3]. Reddy et al [6] reported that, in an aerial glyphosate drift experiment, chlorophyll reduction is about 80% for non-GR soybean leaves sprayed with 0.866 kg·ae/ha glyphosate solution, and higher than 40% for non-GR cotton leaves within one week after treatment. These changes could be attributed to the biochemical effects of glyphosate
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