Journal or Publishing Institution: Remote Sensing
Study: https://web.archive.org/web/20180816044904/http://www.mdpi.com/2072-4292/6/2/1538/htm
Author(s): Zhao, F., Huang, Y., Guo, Y., Reddy, K.N., Lee, M.A., Fletcher, R.S. and Thomson, S.J.
Article Type: Peer Reviewed Study
Record ID: 1749
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.
Keywords: Crop Injury, Herbicide, Glyphosate, Leaf Reflectance, Spectral Indices, Sensitivity Analysis, Canonical Analysis; Hyperspectral Imaging Systems, Pesticides, Coating Processes, Plant Diseases, Forage Plants, Mine Valuation, Crop Injuries; Canonical Analysis,
Citation: Zhao, F., Huang, Y., Guo, Y., Reddy, K.N., Lee, M.A., Fletcher, R.S. and Thomson, S.J., 2014. Early detection of crop injury from glyphosate on soybean and cotton using plant leaf hyperspectral data. Remote Sensing, 6(2), pp.1538-1563.
