# Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Experiment Design and Data Measurement

_{2}-pressurized backpack sprayer that delivered 140 L/ha of spray solution at 193 kPa. After the glyphosate spray, leaf reflectance and biochemical parameters (detailed explanations in Section 2.3): Chlorophyll content (Chl), Equivalent Water Thickness (EWT) and Leaf Mass per Area (LMA) of three plants for each group were measured at 6, 24, 48, 72 h After the Treatment (HAT) to study plant response to glyphosate.

_{s}), stray light measurement (I

_{d}), and Reference Standard measurement (I

_{r}). These spectra were collected in raw DN (Digital Number) mode. An integration time of 544 ms was used for all the measurements. With the known reflectance of the Reference Standard, R

_{r}, the reflectance of the sample for a given center wavelength and spectral bandpass, R

_{s}, is calculated as follows:

#### 2.2. Spectral Indices for Glyphosate Injury Detection

#### 2.3. Spectral Band Selection Based on Sensitivity Analysis of PROSPECT Model

_{a+b}, chlorophyll a+b content, in unit of μg/cm

^{2}), EWT (C

_{w}, mass of water per leaf area, in unit of g/cm

^{2}) and LMA (C

_{m}, mass of dry matter per leaf area, in unit of g/cm

^{2}). The model has been widely used to investigate the relationship between leaf biochemical parameters and reflectance spectrum for a wide range of species during the last two decades [35]. Therefore, PROSPECT was employed in the study to investigate the sensitive spectral domains for the biochemical sensitivity test.

_{i}, the total variance V

_{t}of the model output Y can be decomposed as

_{i}, one should first estimate the Expectation (E) of Y for a fixed value of X

_{i}(i.e., to calculate E(Y|X

_{i})), and then estimate the Variance (V) of E(Y|X

_{i}) for different values of X

_{i}, which could be expressed as

_{i,j}, V

_{i,j,m}, … could be calculated as

_{i}, its first order sensitivity index (S

_{Xi}) and total sensitivity index (S

_{TXi}) are defined as

_{i}. Thus the EFAST method was employed for the following sensitivity analysis of leaf biochemical parameters. The calculations were programmed using Visual C++.

#### 2.4. Feature Extraction Procedure

_{A}is the standard deviation among groups and σ

_{B}is the standard deviations within the groups) is as large as possible, and thus the largest possibility will be generated to discriminate injured leaves from healthy ones.

#### 2.5. Statistical Analysis

## 3. Results and Discussion

#### 3.1. Variations in Leaf Biochemical Contents after Treatment

#### 3.2. Variations in Spectral Indices after Treatment

#### 3.3. FCA Feature Extraction

_{a+b}, C

_{w}, and C

_{m}were defined as 2.8086–19.106 μg/cm

^{2}, 0.0098–0.0267 g/cm

^{2}and 0.0018–0.0045 g/cm

^{2}, respectively, since the ranges could cover all greenhouse measured values in Dataset I and Dataset II. N was assigned a reasonable range of 1–4, which could describe a wide range of mesophyll structures of different leaf species. A thousand combinations of the parameters were randomly selected from their ranges as the inputs and 1,000 reflectance spectra were produced from model simulation. All the simulated spectral reflectance combined with the corresponding selected values of input parameters were used as input data for the test. The results of the PROSPECT sensitivity analysis of leaf biochemical parameters are shown in Figure 2. It was found that N was the most sensitive parameter within the entire spectral region from 400 nm to 2,500 nm, and was relatively more sensitive in the near-infrared range (670–1,300 nm). C

_{a+b}was the most sensitive parameter in the visible bands (400–670 nm), with low sensitivity in other bands. C

_{w}was comparatively more sensitive in the shortwave-infrared band (1,300–2,500 nm), especially at the wavelengths corresponding to the water absorption peaks. C

_{m}was a relatively insensitive parameter with sensitivities lower than 0.2 at most wavelengths. These results are consistent with the former studies [24,42].

_{a+b}respectively were selected (Figure 2a). Similarly, two other bands, 654 nm and 673 nm, located at the peaks in the red domain were selected. In the near-infrared domains, leaf structural parameter N, C

_{w}and C

_{m}are more sensitive (Figure 2). Since EWT and LMA of the three groups showed no significant difference 6–72 HAT, only the band for N was considered in this domain. Corresponding to the peaks of the first order and total sensitivity index of N positioned at the same band, the fifth band (750 nm) was selected where the chlorophyll sensitivity was close to zero. This band could potentially provide the information of leaf structure instead of chlorophyll content [43]. The locations of these selected spectral bands are shown in Figure 2a.

^{T}. For cotton, the largest λ was 0.18, and the corresponding eigenvector d was (0.26, 0.65, 0.64, 0.33, 0.03)

^{T}. Therefore, the FCA features (FCA

_{s}for soybean and FCA

_{c}for cotton) could be expressed by the following linear combinations of the selected five spectral bands:

_{1}, R

_{2}, R

_{3}, R

_{4}, and R

_{5}R

_{λ5}are the reflectance values at 479 nm, 508 nm, 652 nm, 673 nm, and 750 nm, respectively.

#### 3.4. Leaf Stress Detection by FCA Feature

_{s}for soybean leaves of all three groups. It could be seen that at 6 HAT, there were no noticeable differences among the FCA

_{s}values of the three groups, which is reasonable considering damage would not be detectable. At and beyond 24 HAT, differences appear more and more pronounced, with a consistent trend of higher spray rate exhibiting larger FCA

_{s}values. A similar trend was observed for cotton in Figure 3b, with larger differences among these three groups from 24 HAT to 72 HAT, compared with the result of soybeans.

_{s}of the 0.5X group was significantly different from that of the CTRL group at 24 HAT, but the FCA

_{s}of the 0.25X group was neither significantly different from that of the CTRL group nor that of the 0.5X group. At and beyond 48 HAT, the three groups could be totally distinguished by FCA

_{s}, with significant differences. For cotton (Table 7), FCA

_{c}of 0.25X and 0.5X groups were significantly different from that of the CTRL group but not significantly different from each other. Similar to soybean, the three groups of cotton could be distinguished by FCA

_{c}at and beyond 48 HAT.

#### 3.5. Cross Validation for FCA Feature

_{s}values of soybean leaves shown in Table 10 (from 6 HAT to 48 HAT), the accuracy for each group increased from below 50% to 100%. At and beyond 48 HAT, all leaf samples could be accurately classified into the correct group. For FCA

_{c}values for cotton leaves shown in Table 11, a similar result was obtained that tended to exhibit less false classification probability with time. At 6 HAT it was difficult to differentiate the treatment groups with the FCA features. Starting from 24 HAT, accuracies of classification increased with time and reached 100% for all groups 48 HAT. These results are consistent with cross validation results of the Duncan’s multiple range tests, which illustrate that the three groups could be totally distinguished from each other by FCA features at and beyond 48 HAT.

#### 3.6. Injury Detection Success by FCA Features

_{s}for soybean and FCA

_{c}for cotton) and the leaf chlorophyll content (in unit of μg/cm

^{2}) measured were analyzed (Figure 4). Results showed that FCA features and leaf chlorophyll content were well correlated. The correlation coefficients were 0.69 for soybean and 0.66 for cotton, and the RMSE (Root Mean Square Error) values were 1.21 μg/cm

^{2}for soybean and 2.04 μg/cm

^{2}for cotton. This indicated that FCA features were capable of reflecting chlorophyll reduction caused by glyphosate treatment. This could be one of the major reasons why glyphosate injury could be successfully detected by FCA features. The second main reason may be attributed to the good separability provided by canonical analysis process, since the first canonical axis, which corresponds to the largest eigenvalue, contained the maximum separation among the three groups. Consequently, these newly extracted spectral features were capable of detecting the onset of glyphosate injury. Compared with FCA features, the traditionally used spectral indices were poorly related to leaf chlorophyll content in our study. The highest correlation coefficients were acquired between NDVI and Chl, with 0.55 for soybean and 0.51 for cotton. In a study aiming to determine the glyphosate-induced stress level of soybean and cotton leaves, Huang et al. [2] attempted to construct the relationships between leaf stress and spectral indices such as NDVI, RVI, SAVI and DVI to separate the high-dose (0.433 kg·ae/ha) and low-dose (0.217 kg·ae/ha) glyphosate-treated leaves from the untreated ones. However, these indices did not always indicate good divisibility, and separation results were not consistent with time. Our results lead to a similar conclusion with a larger group of spectral indices, which confirms the difficulty in relating spectral indices to glyphosate-induced leaf stress level. These results indicate that spectral indices are less effective for detecting the onset of glyphosate injury. Since glyphosate is phytotoxic to crops by an unknown mechanism, further studies are needed to fully explore the biochemical basis of the relationship between glyphosate injury and leaf reflectance spectrum.

#### 3.7. Advantages and Potential of the FCA Features

## 4. Conclusions

_{s}for soybean and FCA

_{c}for cotton), non-GR crop injury caused by glyphosate could be detected shortly after the spray by plant leaf reflectance spectra. The results indicate that the glyphosate injury could be detected by NDVI, RVI, SAVI, and DVI 48 HAT for soybean and 72 HAT for cotton, and the other spectral indices either showed little useful information for separation (dg, dG, dRE, CGFN, EGFN, NPQI, and SFDR), or did not show consistent results for soybean and cotton (WRE, PRI and NPCI). We have also demonstrated that, compared against those traditionally used spectral indices, the FCA features extracted by the canonical analysis technique were superior at early detection of glyphosate injury for non-GR soybean and non-GR cotton leaves, with a consistent trend of higher spraying rate corresponding to higher injury. This trend was more pronounced with time. The three groups with different spray rates showed some separability at 24 HAT by the FCA features and could be distinguished at, and beyond, 48 HAT for both soybean and cotton. Moreover, the spectral bands used in the FCA features were selected based on the sensitivity analysis results of a leaf RT model (leaf optical PROperty SPECTra model, PROSPECT), which can extend the effectiveness of these features to a wide range of leaf structures and growing conditions. These results demonstrate the feasibility of using leaf hyperspectral reflectance measurements for the early detection of glyphosate injury through these newly proposed FCA features.

## Acknowledgments

## Author Contributions

## Disclaimer

## Conflicts of Interest

## References

- Bellaloui, N.; Reddy, K.N.; Zablotowicz, R.M.; Mengistu, A. Simulated glyphosate drift influences nitrate assimilation and nitrogen fixation in non-glyphosate-resistant soybean. J. Agric. Food Chem
**2006**, 54, 3357–3364. [Google Scholar] - Huang, Y.; Thomson, S.J.; Molin, W.T.; Reddy, K.N.; Yao, H. Early detection of soybean plant injury from glyphosate by measuring chlorophyll reflectance and fluorescence. J. Agric. Sci
**2012**, 4, 117–124. [Google Scholar] - Ding, W.; Reddy, K.N.; Krutz, L.J.; Thomson, S.J.; Huang, Y.; Zablotowicz, R.M. Biological response of soybean and cotton to aerial glyphosate drift. J. Crop Improv
**2011**, 25, 291–302. [Google Scholar] - Reddy, K.N.; Hoagland, R.E.; Zablotowicz, R.M. Effect of glyphosate on growth, chlorophyll, and nodulation in glyphosate-resistant and susceptible soybean (Glycine max) varieties. J. New Seeds
**2000**, 2, 37–52. [Google Scholar] - Koger, C.H.; Shaner, D.L.; Krutz, L.J.; Walker, T.W.; Buehring, N.; Henry, W.B.; Thomas, W.E.; Wilcut, J.W. Rice (Oryza sativa) response to drift rates of glyphosate. Pest Manag. Sci
**2005**, 61, 1161–1167. [Google Scholar] - Reddy, K.N.; Ding, W.; Zablotowicz, R.M.; Thomson, S.J.; Huang, Y.; Krutz, L.J. Biological responses to glyphosate drift from aerial application in non-glyphosate-resistant corn. Pest Manag. Sci
**2010**, 66, 1148–1154. [Google Scholar] - Ding, W.; Reddy, K.N.; Zablotowicz, R.M.; Bellaloui, N.; Arnold Bruns, H. Physiological responses of glyphosate-resistant and glyphosate-sensitive soybean to aminomethylphosphonic acid, a metabolite of glyphosate. Chemosphere
**2011**, 83, 593–598. [Google Scholar] - Sammons, D.R.; Tran, M. Examining yellow flash in Roundup ready soybean. North Cent. Weed Sci. Soc. Proc
**2008**, 63, 120. [Google Scholar] - Mamy, L.; Barriuso, E.; Gabrielle, B. Environmental fate of herbicides trifluralin, metazachlor, metamitron and sulcotrione compared with that of glyphosate, a substitute broad spectrum herbicide for different glyphosate-resistant crops. Pest Manag. Sci
**2005**, 61, 905–916. [Google Scholar] - Krezhova, D.D.; Yanev, T.K.; Ivanov, S.V.; Alexieva, V.S. Remote Sensing of the Effect of the Herbicide Glyphosate on the Leaf Spectral Reflectance of Pea Plants (Pisum Sativum l.). In New Developments and Challenges in Remote Sensing; Millpress: Rotterdam, The Netherlands, 2007; pp. 45–52. [Google Scholar]
- Moshou, D.; Bravo, C.; West, J.; Wahlen, S.; McCartney, A.; Ramon, H. Automatic detection of “yellow rust” in wheat using reflectance measurements and neural networks. Comput. Electron. Agric
**2004**, 44, 173–188. [Google Scholar] - Oppelt, N.; Mauser, W. Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. Int. J. Remote Sens
**2004**, 25, 145–159. [Google Scholar] - Duveiller, G.; Weiss, M.; Baret, F.; Defourny, P. Retrieving wheat green area index during the growing season from optical time series measurements based on neural network radiative transfer inversion. Remote Sens. Environ
**2011**, 115, 887–896. [Google Scholar] - Carter, G.A. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Int. J. Remote Sens
**1994**, 15, 697–703. [Google Scholar] - Riaño, D.; Vaughan, P.; Chuvieco, E.; Zarco-Tejada, P.J.; Ustin, S.L. Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: Analysis at leaf and canopy level. IEEE Trans. Geosci. Remote Sens
**2005**, 43, 819–826. [Google Scholar] - Blackburn, G.A. Wavelet decomposition of hyperspectral data: A novel approach to quantifying pigment concentrations in vegetation. Int. J. Remote Sens
**2007**, 28, 2831–2855. [Google Scholar] - Colombo, R.; Meroni, M.; Marchesi, A.; Busetto, L.; Rossini, M.; Giardino, C.; Panigada, C. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sens. Environ
**2008**, 112, 1820–1834. [Google Scholar] - Gamon, J.A.; Peñuelas, J.; Field, C.B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ
**1992**, 41, 35–44. [Google Scholar] - Peñuelas, J.; Gamon, J.A.; Fredeen, A.L.; Merino, J.; Field, C.B. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ
**1994**, 48, 135–146. [Google Scholar] - Blackburn, G.A.; Ferwerda, J.G. Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sens. Environ
**2008**, 112, 1614–1632. [Google Scholar] - Cheng, T.; Rivard, B.; Sanchez-Azofeifa, A. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens. Environ
**2011**, 115, 659–670. [Google Scholar] - Huang, Y.; Thomson, S.J.; Ortiz, B.V.; Reddy, K.N.; Ding, W.; Zablotowicz, R.M.; Bright, J.R. Airborne remote sensing assessment of the damage to cotton caused by spray drift from aerially applied glyphosate through spray deposition measurements. Biosyst. Eng
**2010**, 107, 212–220. [Google Scholar] - Yao, H.; Huang, Y.; Hruska, Z.; Tomson, S.J.; Reddy, K.N. Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate. Comput. Electron. Agric
**2012**, 89, 145–157. [Google Scholar] - Jacquemoud, S.; Baret, F. PROSPECT: A model of leaf optical properties spectra. Remote Sens. Environ
**1990**, 34, 75–91. [Google Scholar] - Integrating Sphere User Manual ASD; Document 600660 Rev. B; ASD Inc: Boulder, CO, USA, 2008.
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ
**1974**, 351, 309–317. [Google Scholar] - Barnes, J.D.; Balaguer, L.; Manrique, E.; Elvira, S.; Davison, A.W. A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environ. Exp. Bot
**1992**, 32, 85–100. [Google Scholar] - Filella, I.; Peñuelas, J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int. J. Remote Sens
**1994**, 15, 1459–1470. [Google Scholar] - Le Maire, G.; François, C.; Dufrene, E. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens. Environ
**2004**, 89, 1–28. [Google Scholar] - Fourty, T.; Baret, F.; Jacquemoud, S.; Schmuck, G.; Verdebout, J. Leaf optical properties with explicit description of its biochemical composition: Direct and inverse problems. Remote Sens. Environ
**1996**, 56, 104–117. [Google Scholar] - Baret, F.; Fourty, T. Estimation of leaf water content and specific leaf weight from reflectance and transmittance measurements. Agronomie
**1997**, 17, 455–464. [Google Scholar] - Jacquemoud, S.; Ustin, S.L.; Verdebout, J.; Schmuck, G.; Andreoli, G.; Hosgood, B. Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sens. Environ
**1996**, 56, 194–202. [Google Scholar] - Jacquemoud, S.; Bacour, C.; Poilve, H.; Frangi, J.P. Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse mode. Remote Sens. Environ
**2000**, 74, 471–481. [Google Scholar] - Feret, J.B.; François, C.; Asner, G.P.; Gitelson, A.A.; Martin, R.E.; Bidel, L.P.; Ustin, S.L.; le Maire, G.; Jacquemoud, S. PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sens. Environ
**2008**, 112, 3030–3043. [Google Scholar] - Jacquemoud, S.; Verhoef, W.; Baret, F.; Bacour, C.; Zarco-Tejada, P.J.; Asner, G.P.; François, C.; Ustin, S.L. PROSPECT + SAIL models: A review of use for vegetation characterization. Remote Sens. Environ
**2009**, 113, S56–S66. [Google Scholar] - Saltelli, A.; Ratto, M.; Andres, T.; Campolongo, F.; Cariboni, J.; Gatelli, D.; Saisana, M.; Tarantola, S. Global Sensitivity Analysis, The Primer; John Wiley & Sons Ltd: West Sussex, UK, 2008. [Google Scholar]
- Cukier, R.I.; Fortuin, C.M.; Schuler, K.E.; Petschek, A.G.; Schaibly, J.H. Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory. J. Chem. Phys
**1973**, 59, 3873–3878. [Google Scholar] - Cukier, R.I.; Levine, H.B.; Shuler, K.E. Nonlinear sensitivity analysis of multiparameter model systems. J. Comput. Phys
**1978**, 26, 1–42. [Google Scholar] - Koda, M.; Mcrae, G.J.; Seinfeld, J.H. Automatic sensitivity analysis of kinetic mechanisms. Int. J. Chem. Kinet
**1979**, 11, 427–444. [Google Scholar] - Saltelli, A.; Tarantola, S.; Chan, K. A quantitative, model independent method for global sensitivity analysis of model output. Technometrics
**1999**, 41, 39–56. [Google Scholar] - Lejeune, M.; Caliński, T. Canonical analysis applied to multivariate analysis of variance. J. Multivar. Anal
**2000**, 72, 100–119. [Google Scholar] - Li, P.; Wang, Q. Retrieval of leaf biochemical parameters using PROSPECT inversion: A new approach for alleviating ill-posed problems. IEEE Trans. Geosci. Remote Sens
**2011**, 49, 2499–2506. [Google Scholar] - Knipling, E.B. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens. Environ
**1970**, 1, 155–159. [Google Scholar] - Zhao, F.; Gu, X.; Verhoef, W.; Wang, Q.; Yu, T.; Liu, Q.; Huang, H.; Qin, W.; Chen, L.; Zhao, H. A spectral directional reflectance model of row crops. Remote Sens. Environ
**2010**, 114, 265–285. [Google Scholar]

**Figure 2.**Sensitivity indices of PROSPECT input parameters simulated by EFAST (Extended Fourier Amplitude Sensitivity Test) method.

**(a)**400–1,000 nm,

**(b)**1,000–2,500 nm. FOSI: First Order Sensitivity Index, TSI: Total Sensitivity Index. The spectral positions of these selected bands (479, 508, 654, 673, 750 nm) are marked with vertical dotted lines.

**Figure 3.**(

**a**) FCA

_{s}variation of soybean leaves of the three groups at 6, 24, 48, 72 HAT. (

**b**) FCA

_{c}variation of cotton leaves of the three groups at 6, 24, 48, 72 HAT. Each point is a mean value of six leaves for the same treatment. Error bar presents the standard deviation of each point.

**Table 1.**Leaf biochemical measurements of soybean and cotton leaves acquired in the first experiment conducted on 17–20 December 2012. The mean values and standard deviations of the biochemical parameters (Chlorophyll content, Chl; Equivalent Water Thickness, EWT; Leaf Mass per Area, LMA) measured for each treatment group (CTRL group with no glyphosate treatment; 0.25X group treated with 0.217 kg·ae/ha solution of glyphosate; 0.5X group treated with 0.433 kg·ae/ha solution of glyphosate) at each time period after treatment (6, 24, 48, and 72 h After Treatment (HAT)) are shown in the table. The separation analysis of the mean values was conducted using Duncan’s multiple range test.

^{*}

Crop | Soybean | Cotton | |||||
---|---|---|---|---|---|---|---|

Group | CTRL | 0.25X | 0.5X | CTRL | 0.25X | 0.5X | |

Chl (μg/cm^{2})Mean/Standard deviation of 3 leaves in the same group | 6 HAT | 12.2172 ^{a}/0.6027 | 13.1233 ^{a}/0.5775 | 14.1676 ^{a}/0.3419 | 10.3935 ^{a}/0.6027 | 10.0439 ^{a}/0.5775 | 9.9327 ^{a}/0.3419 |

24 HAT | 13.2172 ^{a}/0.3601 | 13.0325 ^{a}/0.5403 | 12.7367 ^{b}/0.6982 | 9.9763 ^{a}/0.3601 | 9.1208 ^{ab}/0.5403 | 8.5738 ^{b}/0.6982 | |

48 HAT | 13.4515 ^{a}/0.3874 | 10.0867 ^{b}/0.3066 | 9.1455 ^{c}/0.7285 | 10.3043 ^{a}/0.3874 | 9.0895 ^{b}/0.3066 | 7.9085 ^{c}/0.7285 | |

72 HAT | 13.8702 ^{a}/0.4047 | 9.5917 ^{b}/0.3931 | 7.8026 ^{c}/0.4269 | 10.1291 ^{a}/0.4047 | 8.2778 ^{b}/0.3931 | 6.3582 ^{c}/0.4270 | |

EWT (g/cm^{2})Mean/Standard deviation of 3 leaves in the same group | 6 HAT | 0.0121 ^{a}/0.0002 | 0.0121 ^{a}/0.0005 | 0.0120 ^{a}/0.0008 | 0.0167 ^{a}/0.0003 | 0.0169 ^{a}/0.0005 | 0.0167 ^{a}/0.0008 |

24 HAT | 0.0119 ^{a}/0.0007 | 0.0122 ^{a}/0.0007 | 0.0123 ^{a}/0.0003 | 0.0174 ^{a}/0.0007 | 0.0169 ^{a}/0.0007 | 0.0174 ^{a}/0.0003 | |

48 HAT | 0.0119 ^{a}/0.0004 | 0.0120 ^{a}/0.0005 | 0.0123 ^{a}/0.0006 | 0.0179 ^{a}/0.0005 | 0.0182 ^{a}/0.0005 | 0.0182 ^{a}/0.0006 | |

72 HAT | 0.0120 ^{a}/0.0006 | 0.0123 ^{a}/0.0008 | 0.0124 ^{a}/0.0004 | 0.0180 ^{a}/0.0006 | 0.0178 ^{a}/0.0008 | 0.0184 ^{a}/0.0004 | |

LMA (g/cm^{2})Mean/Standard deviation of 3 leaves in the same group | 6 HAT | 0.0024 ^{a}/0.0001 | 0.0024 ^{a}/0.0001 | 0.0024 ^{a}/0.0001 | 0.0029 ^{a}/0.0001 | 0.0028 ^{a}/0.0002 | 0.0027 ^{a}/0.0001 |

24 HAT | 0.0023 ^{a}/0.0001 | 0.0024 ^{a}/0.0001 | 0.0024 ^{a}/0.0001 | 0.0029 ^{a}/0.0001 | 0.0030 ^{a}/0.0002 | 0.0028 ^{a}/0.0001 | |

48 HAT | 0.0023 ^{a}/0.0001 | 0.0023 ^{a}/0.0001 | 0.0024 ^{a}/0.0001 | 0.0030 ^{a}/0.0001 | 0.0031 ^{a}/0.0001 | 0.0033 ^{a}/0.0001 | |

72 HAT | 0.0022 ^{a}/0.0001 | 0.0023 ^{a}/0.0001 | 0.0024 ^{a}/0.0001 | 0.0030 ^{a}/0.0001 | 0.0031 ^{a}/0.0001 | 0.0032 ^{a}/0.0001 |

^{*}means with the same letter are not significantly different at 0.05 level of probability.

**Table 2.**Leaf biochemical measurements of soybean and cotton leaves acquired in the second experiment conducted on 4–7 February 2013. The mean values and standard deviations of the biochemical parameters (Chlorophyll content, Chl; Equivalent Water Thickness, EWT; Leaf Mass per Area, LMA) measured for each treatment group (CTRL group with no glyphosate treatment; 0.25X group treated with 0.217 kg·ae/ha solution of glyphosate; 0.5X group treated with 0.433 kg·ae/ha solution of glyphosate) at each time period after treatment (6, 24, 48, and 72 h After Treatment (HAT)) are shown in the table. The separation analysis of the mean values was conducted using Duncan’s multiple range test.

^{*}

Crop | Soybean | Cotton | |||||
---|---|---|---|---|---|---|---|

Group | CTRL | 0.25X | 0.5X | CTRL | 0.25X | 0.5X | |

Chl (μg/cm^{2})Mean/Standard deviation of 3 leaves in the same group | 6 HAT | 13.3673 ^{a}/0.4227 | 13.2032 ^{a}/0.7414 | 13.1985 ^{a}/0.2118 | 9.8102 ^{a}/0.4227 | 9.9867 ^{a}/0.7414 | 9.9867 ^{a}/0.2118 |

24 HAT | 12.2172 ^{b}/0.3440 | 13.2699 ^{a}/0.3515 | 11.5416 ^{b}/0.4833 | 9.5656 ^{a}/0.3439 | 9.6951 ^{a}/0.3515 | 9.5951 ^{a}/0.4833 | |

48 HAT | 13.9737 ^{a}/0.5056 | 10.5587 ^{b}/0.6570 | 9.3648 ^{c}/0.6506 | 10.5367 ^{a}/0.5056 | 9.0423 ^{b}/0.6570 | 7.8207 ^{c}/0.6506 | |

72 HAT | 14.3165 ^{a}/0.3541 | 9.9559 ^{b}/0.3172 | 7.8462 ^{c}/0.4954 | 10.3765 ^{a}/0.3541 | 7.8915 ^{b}/0.3172 | 6.5954 ^{c}/0.4954 | |

EWT (g/cm^{2})Mean/Standard deviation of 3 leaves in the same group | 6 HAT | 0.0118 ^{a}/0.0003 | 0.0120 ^{a}/0.0008 | 0.0120 ^{a}/0.0003 | 0.0166 ^{a}/0.0003 | 0.0165 ^{a}/0.0008 | 0.0166 ^{a}/0.0003 |

24 HAT | 0.0120 ^{a}/0.0007 | 0.0123 ^{a}/0.0003 | 0.0124 ^{a}/0.0003 | 0.0172 ^{a}/0.0006 | 0.0168 ^{a}/0.0003 | 0.0175 ^{a}/0.0003 | |

48 HAT | 0.0119 ^{a}/0.0004 | 0.0125 ^{a}/0.0007 | 0.0126 ^{a}/0.0005 | 0.0177 ^{a}/0.0003 | 0.0177 ^{a}/0.0006 | 0.0184 ^{a}/0.0005 | |

72 HAT | 0.0118 ^{a}/0.0007 | 0.0122 ^{a}/0.0007 | 0.0125 ^{a}/0.0004 | 0.0182 ^{a}/0.0004 | 0.0175 ^{a}/0.0007 | 0.0183 ^{a}/0.0004 | |

LMA (g/cm^{2})Mean/Standard deviation of 3 leaves in the same group | 6 HAT | 0.0023 ^{a}/0.0001 | 0.0023 ^{a}/0.0001 | 0.0023 ^{a}/0.0001 | 0.0029 ^{a}/0.0001 | 0.0028 ^{a}/0.0001 | 0.0026 ^{a}/0.0001 |

24 HAT | 0.0023 ^{a}/0.0002 | 0.0024 ^{a}/0.0001 | 0.0024 ^{a}/0.0002 | 0.0030 ^{a}/0.0002 | 0.0031 ^{a}/0.0001 | 0.0028 ^{a}/0.0002 | |

48 HAT | 0.0022 ^{a}/0.0001 | 0.0023 ^{a}/0.0002 | 0.0024 ^{a}/0.0001 | 0.0030 ^{a}/0.0001 | 0.0032 ^{a}/0.0002 | 0.0032 ^{a}/0.0001 | |

72 HAT | 0.0023 ^{a}/0.0001 | 0.0023 ^{a}/0.0001 | 0.0023 ^{a}/0.0001 | 0.0029 ^{a}/0.0001 | 0.0031 ^{a}/0.0001 | 0.0031 ^{a}/0.0001 |

^{*}means with the same letter are not significantly different at 0.05 level of probability.

Index | Definition |
---|---|

NDVI ^{*} | $\frac{{R}_{800}-{R}_{680}}{{R}_{800}+{R}_{680}}$, Normalized Difference Vegetation Index |

RVI ^{*} | $\frac{{R}_{800}}{{R}_{680}}$, Ratio Vegetation Index |

SAVI ^{*} | $\frac{{R}_{800}-{R}_{680}}{{R}_{800}+{R}_{680}+L}\cdot (1+L)$, where L = 0.5, Soil Adjusted Vegetation Index |

DVI ^{*} | R_{800} − R_{680}, Difference Vegetation Index |

dg ^{**} | minimum amplitude of the first derivative reflectance in the green region, at approx. 570 nm |

dG ^{**} | maximum amplitude of the first derivative reflectance in the green region, at approx. 525 nm |

dRE ^{**} | maximum amplitude of the first derivative reflectance in the red-edge region, at approx. 700–710 nm |

CGFN ^{**} | $\frac{\mathit{dG}-\mathit{dg}}{\mathit{dG}+\mathit{dg}}$, normalized difference between dG and dg |

EGFN ^{**} | $\frac{\mathit{dRE}-\mathit{dG}}{\mathit{dRE}+\mathit{dG}}$, normalized difference between dRE and dG |

WRE ^{**} | Wavelength position of the Red Edge (i.e., the maximum amplitude wavelength position of the first derivative reflectance in the red-edge region) |

PRI ^{**} | $\frac{{\text{R}}_{550}-{\text{R}}_{530}}{{\text{R}}_{550}+{\text{R}}_{530}}\frac{{R}_{550}-{R}_{530}}{{R}_{550}+{R}_{530}}$, Physiological Reflectance Index |

NPCI ^{**} | $\frac{{\text{R}}_{680}-{\text{R}}_{430}}{{\text{R}}_{680}+{\text{R}}_{430}}\frac{{R}_{680}-{R}_{430}}{{R}_{680}+{R}_{430}}$, Normalized Pigments Chlorophyll ratio Index |

NPQI ^{***} | $\frac{{\text{R}}_{415}-{\text{R}}_{435}}{{\text{R}}_{415}+{\text{R}}_{435}}\frac{{R}_{415}-{R}_{435}}{{R}_{415}+{R}_{435}}$, Normalized Phaeophytinization Quotient Index |

SFDR ^{****} | Sum of the First Derivative Reflectance between 680 nm and 780 nm |

**Table 4.**Calculated spectral indices of the three groups of soybean leaves at 6, 24, 48, 72 HAT. Each value is a mean of six leaves. The separation results are based on the Duncan’s multiple range test.

^{*}

HAT (h) | Index | CTRL Group | 0.25X Group | 0.5X Group |
---|---|---|---|---|

6 | NDVI | 0.8187 ^{a} | 0.8205 ^{a} | 0.8167 ^{a} |

RVI | 10.0944 ^{a} | 10.1608 ^{a} | 9.9265 ^{a} | |

SAVI | 0.5963 ^{a} | 0.5910 ^{a} | 0.5881 ^{a} | |

DVI | 0.3862 ^{a} | 0.3789 ^{a} | 0.3781 ^{a} | |

dg | −0.0024 ^{a} | −0.0024 ^{a} | −0.0025 ^{a} | |

dG | 0.0033 ^{a} | 0.0033 ^{a} | 0.0034 ^{a} | |

dRE | 0.0104 ^{a} | 0.0101 ^{a} | 0.0102 ^{a} | |

CGFN | 6.5295 ^{a} | 6.328 ^{a} | 6.4118 ^{a} | |

EGFN | 0.5173 ^{a} | 0.5079 ^{a} | 0.5027 ^{a} | |

WRE ^{**} | 709 ^{a} | 707 ^{a} | 705 ^{a} | |

PRI | 0.0198 ^{a} | 0.0198 ^{a} | 0.0204 ^{a} | |

NPCI | 0.0016 ^{a} | 0.0016 ^{a} | −0.0012 ^{a} | |

NPQI | −0.0358 ^{a} | −0.0368 ^{a} | −0.0376 ^{a} | |

SFDR | 0.3876 ^{a} | 0.3805 ^{a} | 0.3733 ^{a} | |

24 | NDVI | 0.8229 ^{a} | 0.8157 ^{a} | 0.8187 ^{a} |

RVI | 10.3650 ^{a} | 10.2972 ^{a} | 10.4699 ^{a} | |

SAVI | 0.5839 ^{a} | 0.5805 ^{a} | 0.5875 ^{a} | |

DVI | 0.3694 ^{a} | 0.3723 ^{a} | 0.3794 ^{a} | |

dg | −0.0024 ^{a} | −0.0025 ^{a} | −0.0025 ^{a} | |

dG | 0.0033 ^{a} | 0.0035 ^{a} | 0.0036 ^{a} | |

dRE | 0.0099 ^{a} | 0.0102 ^{a} | 0.0106 ^{a} | |

CGFN | 6.1602 ^{a} | 6.0017 ^{a} | 5.9571 ^{a} | |

EGFN | 0.5025 ^{a} | 0.4888 ^{a} | 0.4975 ^{a} | |

WRE ^{**} | 706 ^{a} | 706 ^{a} | 706 ^{a} | |

PRI | 0.0195 ^{a} | 0.0201 ^{a} | 0.0211 ^{a} | |

NPCI | 0.0402 ^{a} | 0.0452 ^{a} | 0.0493 ^{a} | |

NPQI | −0.0337 ^{a} | −0.0291 ^{a} | −0.0343 ^{a} | |

SFDR | 0.3688 ^{a} | 0.3728 ^{a} | 0.3779 ^{a} | |

48 | NDVI | 0.8357 ^{a} | 0.8137 ^{b} | 0.8057 ^{b} |

RVI | 10.7239 ^{a} | 9.3939 ^{b} | 9.2385 ^{b} | |

SAVI | 0.5887 ^{a} | 0.5860 ^{ab} | 0.5825 ^{b} | |

DVI | 0.3741 ^{a} | 0.3732 ^{b} | 0.3727 ^{b} | |

dg | −0.0022 ^{a} | −0.0026 ^{a} | −0.0026 ^{a} | |

dG | 0.0030 ^{a} | 0.0037 ^{a} | 0.0040 ^{a} | |

dRE | 0.0099 ^{a} | 0.0105 ^{a} | 0.0108 ^{a} | |

CGFN | 5.3727 ^{a} | 5.5219 ^{a} | 5.0925 ^{a} | |

EGFN | 0.5199 ^{a} | 0.4743 ^{a} | 0.4624 ^{a} | |

WRE ^{**} | 704 ^{a} | 702 ^{a} | 702 ^{a} | |

PRI | 0.0069 ^{b} | 0.0155 ^{ba} | 0.0205 ^{a} | |

NPCI | 0.0045 ^{b} | 0.0328 ^{a} | 0.0364 ^{a} | |

NPQI | −0.0314 ^{a} | −0.0305 ^{a} | −0.0403 ^{a} | |

SFDR | 0.3747 ^{a} | 0.3736 ^{a} | 0.3749 ^{a} | |

72 | NDVI | 0.8210 ^{a} | 0.8111 ^{b} | 0.7834 ^{c} |

RVI | 10.1770 ^{a} | 9.6108 ^{b} | 8.4101 ^{c} | |

SAVI | 0.5943 ^{a} | 0.5696 ^{b} | 0.5675 ^{b} | |

DVI | 0.3828 ^{a} | 0.3703 ^{b} | 0.3677 ^{b} | |

dg | −0.0023 ^{a} | −0.0026 ^{a} | −0.0027 ^{a} | |

dG | 0.0031 ^{a} | 0.0037 ^{a} | 0.0045 ^{a} | |

dRE | 0.0101 ^{a} | 0.0104 ^{a} | 0.0118 ^{a} | |

CGFN | 6.4162 ^{a} | 5.8877 ^{a} | 4.4841 ^{a} | |

EGFN | 0.5426 ^{a} | 0.4797 ^{a} | 0.4489 ^{a} | |

WRE ^{**} | 711 ^{a} | 703 ^{b} | 701 ^{b} | |

PRI | 0.0013 ^{c} | 0.0117 ^{b} | 0.0212 ^{a} | |

NPCI | 0.0153 ^{c} | 0.0328 ^{b} | 0.0876 ^{a} | |

NPQI | −0.0329 ^{a} | −0.0357 ^{a} | −0.0361 ^{a} | |

SFDR | 0.3839 ^{a} | 0.3799 ^{a} | 0.3709 ^{a} |

^{*}means with the same letter are not significantly different in each row at 0.05 level of probability;

^{**}units of nm.

**Table 5.**Calculated spectral indices of the three groups of cotton leaves at 6, 24, 48, 72 HAT. Each value is a mean of six leaves. The separation results are based on the Duncan’s multiple range test.

^{*}

HAT (h) | Index | CTRL Group | 0.25X Group | 0.5X Group |
---|---|---|---|---|

6 | NDVI | 0.7908 ^{a} | 0.7886 ^{a} | 0.7853 ^{a} |

RVI | 8.5872 ^{a} | 8.5408 ^{a} | 8.5376 ^{a} | |

SAVI | 0.5888 ^{a} | 0.5860 ^{a} | 0.5799 ^{a} | |

DVI | 0.3903 ^{a} | 0.3921 ^{a} | 0.3854 ^{a} | |

dg | −0.0025 ^{a} | −0.0028 ^{a} | −0.0027 ^{a} | |

dG | 0.0034 ^{a} | 0.0040 ^{a} | 0.0039 ^{a} | |

dRE | 0.0108 ^{a} | 0.0114 ^{a} | 0.0112 ^{a} | |

CGFN | 5.4463 ^{a} | 5.3628 ^{a} | 5.3454 ^{a} | |

EGFN | 0.5007 ^{a} | 0.4777 ^{a} | 0.4810 ^{a} | |

WRE ^{**} | 703 ^{a} | 700 ^{a} | 701 ^{a} | |

PRI | 0.0206 ^{a} | 0.0212 ^{a} | 0.0208 ^{a} | |

NPCI | 0.0957 ^{a} | 0.0976 ^{a} | 0.1025 ^{a} | |

NPQI | 0.0023 ^{a} | 0.0020 ^{a} | 0.0028 ^{a} | |

SFDR | 0.3899 ^{a} | 0.3921 ^{a} | 0.3816 ^{a} | |

24 | NDVI | 0.7936 ^{a} | 0.7917 ^{a} | 0.7880 ^{a} |

RVI | 8.7672 ^{a} | 8.6153 ^{a} | 8.4444 ^{a} | |

SAVI | 0.5857 ^{a} | 0.5876 ^{a} | 0.5769 ^{a} | |

DVI | 0.3849 ^{a} | 0.3878 ^{a} | 0.3760 ^{a} | |

dg | −0.0025 ^{a} | −0.0026 ^{a} | −0.0024 ^{a} | |

dG | 0.0037 ^{a} | 0.0036 ^{a} | 0.0034 ^{a} | |

dRE | 0.0107 ^{a} | 0.0106 ^{a} | 0.0106 ^{a} | |

CGFN | 5.9509 ^{a} | 6.1741 ^{a} | 6.2811 ^{a} | |

EGFN | 0.4877 ^{a} | 0.4920 ^{a} | 0.5157 ^{a} | |

WRE ^{**} | 702 ^{a} | 704 ^{a} | 704 ^{a} | |

PRI | 0.0175 ^{a} | 0.0165 ^{a} | 0.0168 ^{a} | |

NPCI | 0.0673 ^{a} | 0.0640 ^{a} | 0.0659 ^{a} | |

NPQI | 0.0112 ^{a} | 0.0103 ^{a} | 0.0093 ^{a} | |

SFDR | 0.3855 ^{a} | 0.3884 ^{a} | 0.3764 ^{a} | |

48 | NDVI | 0.7890 ^{a} | 0.7828 ^{a} | 0.7865 ^{a} |

RVI | 8.4550 ^{a} | 8.4173 ^{a} | 8.4373 ^{a} | |

SAVI | 0.5786 ^{a} | 0.5813 ^{a} | 0.5843 ^{a} | |

DVI | 0.3832 ^{a} | 0.3839 ^{a} | 0.3857 ^{a} | |

dg | −0.0026 ^{a} | −0.0025 ^{a} | −0.0026 ^{a} | |

dG | 0.0037 ^{a} | 0.0036 ^{a} | 0.0036 ^{a} | |

dRE | 0.0109 ^{a} | 0.0105 ^{a} | 0.0108 ^{a} | |

CGFN | 6.0213 ^{a} | 5.9717 ^{a} | 6.1377 ^{a} | |

EGFN | 0.4933 ^{a} | 0.4937 ^{a} | 0.5035 ^{a} | |

WRE ^{**} | 703 ^{a} | 703 ^{a} | 703 ^{a} | |

PRI | 0.0155 ^{b} | 0.0201 ^{ba} | 0.0219 ^{a} | |

NPCI | 0.0898 ^{a} | 0.0843 ^{a} | 0.0783 ^{a} | |

NPQI | 0.0013 ^{a} | 0.0018 ^{a} | 0.0015 ^{a} | |

SFDR | 0.3841^{a} | 0.3835 ^{a} | 0.3843 ^{a} | |

72 | NDVI | 0.8015 ^{a} | 0.7874 ^{b} | 0.7841 ^{b} |

RVI | 9.1241 ^{a} | 8.4317 ^{b} | 8.4278 ^{b} | |

SAVI | 0.5827 ^{a} | 0.5433 ^{b} | 0.5364 ^{b} | |

DVI | 0.3670 ^{a} | 0.3517 ^{b} | 0.3482 ^{b} | |

dg | −0.0023 ^{a} | −0.0024 ^{a} | −0.0024 ^{a} | |

dG | 0.0031 ^{a} | 0.0033 ^{a} | 0.0033 ^{a} | |

dRE | 0.0101 ^{a} | 0.0106 ^{a} | 0.0104 ^{a} | |

CGFN | 7.0570 ^{a} | 7.0137 ^{a} | 6.8710 ^{a} | |

EGFN | 0.5340 ^{a} | 0.5269 ^{a} | 0.5211 ^{a} | |

WRE ^{**} | 707 ^{a} | 706 ^{a} | 706 ^{a} | |

PRI | 0.0136 ^{a} | 0.0149 ^{a} | 0.0196 ^{a} | |

NPCI | 0.0772 ^{a} | 0.0827 ^{a} | 0.0784 ^{a} | |

NPQI | 0.040 ^{a} | −0.0038 ^{a} | 0.0045 ^{a} | |

SFDR | 0.3798 ^{a} | 0.3958 ^{a} | 0.3894 ^{a} |

^{*}means with the same letter are not significantly different in each row at 0.05 level of probability;

^{**}units of nm.

**Table 6.**FCA

_{s}calculated for experimental soybean leaves of the three groups at 6, 24, 48, 72 HAT. Each value is a mean of six leaves for the same group. The statistics are analyzed by Duncan’s multiple range tests.

^{*}

HAT (h) | CTRL Group | 0.25X Group | 0.5X Group |
---|---|---|---|

6 | 0.07304 ^{a} | 0.07288 ^{a} | 0.07173 ^{a} |

24 | 0.07123 ^{b} | 0.07433 ^{ba} | 0.07700 ^{a} |

48 | 0.07157 ^{c} | 0.07880 ^{b} | 0.08265 ^{a} |

72 | 0.07485 ^{c} | 0.08182 ^{b} | 0.08899 ^{a} |

^{*}means with the same letter are not significantly different in each row at 0.05 level of probability.

**Table 7.**FCA

_{c}calculated for experimental cotton leaves of the three groups at 6, 24, 48, 72 HAT. Each value is a mean of six leaves for the same group. The statistics are analyzed by Duncan’s multiple range tests

^{*}.

HAT (h) | CTRL Group | 0.25X Group | 0.5X Group |
---|---|---|---|

6 | 0.07004 ^{a} | 0.07452 ^{a} | 0.07315 ^{a} |

24 | 0.07178 ^{b} | 0.08347 ^{a} | 0.08570 ^{a} |

48 | 0.06748 ^{c} | 0.07873 ^{b} | 0.08613 ^{a} |

72 | 0.07177 ^{c} | 0.08287 ^{b} | 0.09011 ^{a} |

^{*}means with the same letter are not significantly different in each row at 0.05 level of probability.

**Table 8.**Cross validation results for FCA

_{s}based on Duncan’s multiple range tests

^{*}. Each value is a mean of three soybean leaves for the same group in the validation dataset.

HAT (h) | CTRL Group | 0.25X Group | 0.5X Group |
---|---|---|---|

Round I | |||

6 | 0.07190 ^{a} | 0.07241 ^{a} | 0.07047 ^{a} |

24 | 0.07001 ^{b} | 0.07525 ^{ab} | 0.07763 ^{a} |

48 | 0.07154 ^{c} | 0.07737 ^{b} | 0.08391 ^{a} |

72 | 0.07349 ^{c} | 0.08061 ^{b} | 0.08962 ^{a} |

Round II | |||

6 | 0.07400 ^{a} | 0.07393 ^{a} | 0.07317 ^{a} |

24 | 0.06983 ^{b} | 0.07394 ^{b} | 0.07602 ^{a} |

48 | 0.07182 ^{c} | 0.07753 ^{b} | 0.08387 ^{a} |

72 | 0.07514 ^{c} | 0.08090 ^{b} | 0.08802 ^{a} |

^{*}means with the same letter are not significantly different in each row at 0.05 level of probability.

**Table 9.**Cross validation results for FCA

_{c}based on Duncan’s multiple range tests

^{*}. Each value is a mean of three cotton leaves for the same group in the validation dataset.

HAT (h) | CTRL Group | 0.25X Group | 0.5X Group |
---|---|---|---|

Round I | |||

6 | 0.06876 ^{b} | 0.07297 ^{a} | 0.07435 ^{a} |

24 | 0.07012 ^{b} | 0.08467 ^{a} | 0.08367 ^{a} |

48 | 0.06606 ^{c} | 0.07719 ^{b} | 0.08365 ^{a} |

72 | 0.07158 ^{c} | 0.08060 ^{b} | 0.09010 ^{a} |

Round II | |||

6 | 0.07181 ^{a} | 0.07642 ^{a} | 0.07428 ^{a} |

24 | 0.07210 ^{b} | 0.08293 ^{a} | 0.08791 ^{a} |

48 | 0.06826 ^{c} | 0.07807 ^{b} | 0.08540 ^{a} |

72 | 0.07358 ^{c} | 0.08110 ^{b} | 0.09058 ^{a} |

^{*}means with the same letter are not significantly different in each row at 0.05 level of probability.

**Table 10.**Linear discriminant analysis with a two-fold cross validation schema for FCA

_{s}values of soybean leaves.

From Group | Number of FCA_{s} Values (Round I + Round II) Classified into Group | Accuracy (%) | ||
---|---|---|---|---|

CTRL | 0.25X | 0.5X | ||

6 HAT | ||||

CTRL | 2 (2 + 0) | 2 (0 + 2) | 2 (1 + 1) | 33 |

0.25X | 3 (1 + 2) | 1 (1 + 0) | 2 (1 + 1) | 17 |

0.5X | 1 (0 + 1) | 3 (1 + 2) | 2 (2 + 0) | 33 |

24 HAT | ||||

CTRL | 2 (1 + 1) | 3 (2 + 1) | 1 (0 + 1) | 50 |

0.25X | 1 (1 + 0) | 4 (1 + 3) | 1 (1 + 0) | 67 |

0.5X | 0 (0 + 0) | 1 (1 + 0) | 5 (2 + 3) | 83 |

48 HAT | ||||

CTRL | 6 (3 + 3) | 0 (0 + 0) | 0 (0 + 0) | 100 |

0.25X | 0 (0 + 0) | 6 (3 + 3) | 0 (0 + 0) | 100 |

0.5X | 0 (0 + 0) | 0 (0 + 0) | 6 (3 + 3) | 100 |

72 HAT | ||||

CTRL | 6 (3 + 3) | 0 (0 + 0) | 0 (0 + 0) | 100 |

0.25X | 0 (0 + 0) | 6 (3 + 3) | 0 (0 + 0) | 100 |

0.5X | 0 (0 + 0) | 0 (0 + 0) | 6 (3 + 3) | 100 |

**Table 11.**Linear discriminant analysis with a two-fold cross validation schema for FCA

_{c}values of cotton leaves.

From Group | Number of FCA_{c} Values (Round I + Round II) Classified into Group | Accuracy (%) | ||
---|---|---|---|---|

CTRL | 0.25X | 0.5X | ||

6 HAT | ||||

CTRL | 1 (0 + 1) | 3 (2 + 1) | 2 (1 + 1) | 17 |

0.25X | 3 (1 + 2) | 1 (1 + 0) | 2 (1 + 1) | 17 |

0.5X | 1 (1 + 0) | 3 (2 + 1) | 2 (0 + 2) | 33 |

24 HAT | ||||

CTRL | 5 (2 + 3) | 1 (1 + 0) | 0 (0 + 0) | 83 |

0.25X | 0 (0 + 0) | 4 (2 + 2) | 2 (1 + 1) | 67 |

0.5X | 1 (0 + 1) | 1 (1 + 0) | 4 (2 + 2) | 67 |

48 HAT | ||||

CTRL | 6 (3 + 3) | 0 (0 + 0) | 0 (0 + 0) | 100 |

0.25X | 0 (0 + 0) | 6 (3 + 3) | 0 (0 + 0) | 100 |

0.5X | 0 (0 + 0) | 0 (0 + 0) | 6 (3 + 3) | 100 |

72 HAT | ||||

CTRL | 6 (3 + 3) | 0 (0 + 0) | 0 (0 + 0) | 100 |

0.25X | 0 (0 + 0) | 6 (3 + 3) | 0 (0 + 0) | 100 |

0.5X | 0 (0 + 0) | 0 (0 + 0) | 6 (3 + 3) | 100 |

© 2014 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

## Share and Cite

**MDPI and ACS Style**

Zhao, F.; Huang, Y.; Guo, Y.; Reddy, K.N.; Lee, M.A.; Fletcher, R.S.; Thomson, S.J.
Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data. *Remote Sens.* **2014**, *6*, 1538-1563.
https://doi.org/10.3390/rs6021538

**AMA Style**

Zhao F, Huang Y, Guo Y, Reddy KN, Lee MA, Fletcher RS, Thomson SJ.
Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data. *Remote Sensing*. 2014; 6(2):1538-1563.
https://doi.org/10.3390/rs6021538

**Chicago/Turabian Style**

Zhao, Feng, Yanbo Huang, Yiqing Guo, Krishna N. Reddy, Matthew A. Lee, Reginald S. Fletcher, and Steven J. Thomson.
2014. "Early Detection of Crop Injury from Glyphosate on Soybean and Cotton Using Plant Leaf Hyperspectral Data" *Remote Sensing* 6, no. 2: 1538-1563.
https://doi.org/10.3390/rs6021538