# Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato

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## Abstract

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## 1. Introduction

^{2}between the area under the disease progress curve estimated visually and by the image-based approach, varying between 0.73 and 0.77. Duarte-Carvajalino et al. [39] performed machine learning-based estimation of late blight severity using very high resolution imagery acquired over the growing season, with a modified camera registering blue, red, and NIR bands. Despite using considerably different prediction approaches in comparison with that described by Sugiura et al. [38], the performance reported was similar in both studies. Other authors only described qualitative evaluation of late blight incidence using UAV multispectral imagery [40,41] or only assessed effects of advanced stages of the pathogen development on potato traits and crop spectral response using data acquired by an hyperspectral imaging system [42,43]. However, studies focusing on UAV or other sources of very high resolution imagery (with sub-decimeter resolution), including validation by means of ground truth data (e.g., measurement of crop traits and assessment of disease severity, etc.), and aiming a detailed description of spectral changes related to early pathogen incidence have not been made so far for potato infection with late blight.

## 2. Materials and Methods

#### 2.1. Study Area and Experimental Set-Up

#### 2.2. UAV Optical Imagery with Sub-Decimeter Resolution

^{2}sr nm, was performed using camera manufacturer’s proprietary software (HyperspectralImager, v2.0, Rikola Ltd., Oulu, Finland). This step included the correction for dark current using images taken with the sensor lens completely covered (dark reference), acquired before each flight, and for flat field using factory calibration parameters. Radiance was converted into reflectance factor through the empirical line method using images of a Spectralon reference panel with nominal 50% reflectance (LabSphere Inc., North Sutton, NH, USA). These images were taken immediately before flight under the same general illumination conditions observed during data acquisition.

#### 2.3. Ground-Based Optical Imagery with Sub-Centimeter Resolution

#### 2.4. Estimation of Ground Cover for Background Removal from Ground-Based Imagery

#### 2.5. Mitigation of Background Effects on UAV Imagery Using Vegetation Index Threshold

#### 2.6. Measurements of Crop Traits and Treatments Comprison Based on Linear Mixed Effects Models

^{−2}) was performed based on the equation provided by Uddling et al. [61], and the average for three measurements made within each sampling unit was the final leaf chlorophyll content evaluated. Similarly, canopy height was measured from the potato ridge to the highest leaf in each plant and the average represented the final values used to describe the canopy in each sampling unit. It is worth noting that since leaf chlorophyll content and canopy height were measured in three SUs within each small experimental plot (Figure 1), ground cover was estimated considering the same SUs for all data acquisitions (i.e., from 37 to 78 DAP). Only SUs in the small plots were considered for treatment comparison. It is worth reminding that a more complete dataset (as indicated in Section 2.2 and comprising observations made in the small and large plots) was used to evaluate spectral changes over the growing season (as described in Section 2.7 and Section 2.8).

#### 2.7. Descrition of Crop Canopy Spectral Variability through Simplex Volume Maximization (SiVM)

**W**of base vectors and the coefficients matrix

**H**[8]. This is obtained during analysis by retaining only bases that contribute the most to maximize the volume of the simplex described by the vectors included in

**W**. As a computing efficient alternative to this procedure, Thurau et al. [68,69] proposed the so called Simplex Volume Maximization (SiVM) method, which relies on distance geometry rather than on the simplex volume itself [8,70]. The matrix

**H**of coefficients is obtained through constrained quadratic optimization and describes the optimal abundance of each component

**W**for reconstruction of the spectral signatures being modelled.

**W**to represent the ground-based and UAV–borne datasets to be analyzed was set empirically to 25, as performed by Wahabzada et al. [8] and Thomas et al. [11]. It was verified that this number of bases provided good reconstruction accuracy while avoiding the oversampling of the feature space, which may be beneficial considering reconstruction accuracy [71], but might bring problems during further analysis due to the so called “curse of dimensionality” [72]. Each dataset (i.e., ground-based and UAV images) for a given acquisition date was analyzed separately to mitigate impacts of changes in view of geometry and illumination conditions over time on analysis outputs. As an example, archetypes extracted for the ground-based and UAV datasets acquired 78 DAP are presented in Figure A2. Two pixels were chosen in a UAV image patch acquired on that date. Weights derived for reconstructing these pixel spectra from the archetypes are illustrated for both datasets (UAV- and ground-based images).

**W**), the abundance coefficients (

**H**) obtained for pixels from different treatments (i.e., non-mixed and mixed cultivation systems) or disease severity classes were used to estimate a probability density distribution for each group. In this study, the multivariate Dirichlet distribution was adopted and its parameters were estimated by maximum likelihood. With the probability distribution estimated, pixels in the image were mapped according to specific treatment or severity class based on the Bayes factor (i.e., log-likelihood ratio, in this case, LLR, since no prior information on the data distribution was taken into account), as described by Wahabzada et al. [8]. This mapping was performed comparing the difference between the logarithm of probabilities, for coefficients

**h**corresponding to each spectral signature (i.e., pixel-wise comparison), within distributions from different treatments or severity classes considered.

#### 2.8. Pixel-wise Comparison Framework to Identify Relevant Spectral Information Assciatedto Late Blight Development

**H**for T1 and T2. These probabilities were then compared using the LLR, as described in Section 2.7. Probability distribution corresponding to T1 was considered as hypothesis H1 (“group of interest”) and compared to the null hypothesis H0 (“reference”) represented by probability estimated for T2, since healthier plants were expected to be observed in this treatment. A parallel was made between results obtained for ground data and those observed for UAV-based imagery. However, the latter case comprised considerably more observations (as described in Section 2.2 and Section 2.3).

## 3. Results

#### 3.1. Evaluation of Crop Traits and Disease Severity over the Growing Season

#### 3.2. Assessment of General Spectral Changes Related to Different Cropping Systems and Late Blight Infection

**h**in distribution estimated for T1 in comparison to distribution estimated for T2. Although differences in spectra with relatively stronger relationship with T1 (i.e., higher probability in the distribution for T1), in comparison with spectra having weaker association with this treatment, could be detected by both sensing approaches, the relationship observed between spectral information and the treatment of interest (T1) using UAV imagery was generally less intense. For example, in results corresponding to the first data acquisition (37 DAP), considerable differences in the visible and near-infrared are observed when comparing the first LLR interval (Figure 5a, blue line) with other intervals (Figure 5a, color scale), for ground-based data. However, these differences were attenuated (i.e., lower variation indicated by ratio values and smaller range of LLR) in UAV images (Figure 5b).

#### 3.3. Effects of Specific Late Blight Severity Levels on the Crop Spectral Response

#### 3.4. Spatial Patterns of Visual Disease Assessment Compared with Outputs of Simplex Volume Maximization (SiVM) and Log-Likelihood Ratio Applied to UAV Imagery

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Table A1.**Band-to-band registration accuracy (RMSE in pixels) within and between spectral band subsets for ground-based images. Results are summarized for each acquisition date indicating average (Avrg.) and range of RMSE values observed for the images acquired in a given date (n = 8 sampling units).

Regist. Method ^{1} | Subset ^{2} 1 | Subset ^{2} 1–2 | Subset ^{2} 2 | Subset ^{2} 2–3 | Subset ^{2} 3 | |||||
---|---|---|---|---|---|---|---|---|---|---|

Avrg. | Range | Avrg. | Range | Avrg. | Range | Avrg. | Range | Avrg. | Range | |

37 DAP | ||||||||||

Raw | 4.50 | 0.67–12.73 | 1.51 | 0.83–2.62 | 2.12 | 0.77–5.93 | 2.44 | 0.72–5.41 | 3.39 | 0.57–16.99 |

I | 1.16 | 0.58–2.98 | 0.91 | 0.78–1.05 | 0.94 | 0.60–3.31 | 0.85 | 0.77–0.95 | 0.93 | 0.55–1.77 |

II | 0.87 | 0.51–2.10 | 0.76 | 0.55–1.39 | 0.87 | 0.55–3.38 | 0.63 | 0.56–0.75 | 0.72 | 0.51–1.81 |

50 DAP | ||||||||||

Raw | 7.23 | 0.61–27.08 | 2.19 | 0.95–4.62 | 2.90 | 0.92–18.30 | 2.17 | 1.00–3.47 | 5.14 | 0.89–18.03 |

I | 1.67 | 0.55–4.01 | 0.96 | 0.85 –1.27 | 1.14 | 0.78–2.44 | 1.02 | 0.88–1.30 | 1.24 | 0.65–2.51 |

II | 0.97 | 0.53–2.27 | 0.73 | 0.58–1.08 | 1.01 | 0.71–2.97 | 0.74 | 0.60–1.06 | 0.73 | 0.56–1.24 |

64 DAP | ||||||||||

Raw | 4.94 | 0.52–18.32 | 1.99 | 0.72–3.29 | 2.29 | 0.70–7.53 | 1.91 | 1.14–3.28 | 3.33 | 0.61–10.57 |

I | 1.46 | 0.57–2.66 | 1.00 | 0.83–1.26 | 1.15 | 0.68–1.96 | 0.95 | 0.82–1.26 | 1.00 | 0.64–1.99 |

II | 1.10 | 0.54–2.18 | 1.37 | 0.67–2.17 | 1.34 | 0.66–3.40 | 0.68 | 0.59–0.84 | 0.76 | 0.58–1.28 |

78 DAP | ||||||||||

Raw | 8.24 | 0.72–30.01 | 1.05 | 0.58–1.74 | 2.21 | 0.66–5.31 | 2.18 | 0.87–3.12 | 5.59 | 0.60–24.51 |

I | 2.07 | 0.58–4.56 | 0.98 | 0.67–1.22 | 1.30 | 0.68–3.52 | 1.07 | 0.90–1.28 | 1.52 | 0.65–2.97 |

II | 1.21 | 0.59–2.21 | 0.89 | 0.66–1.41 | 1.13 | 0.69–2.10 | 0.80 | 0.61–1.08 | 0.82 | 0.57–1.63 |

^{1}–Registration (Regist.) methods: without registration (Raw); affine (I); displacement field (II).

^{2}–Subset 1 comprises 12 bands, between 503 and 660 nm; subset 2 is six bands, between 672 and 750; and subset 3 is ten bands, from 763 to 893 nm.

Vegetation Index (VI) | Formulation ^{2} | Acq. Level ^{3} | Sensitivity (Scale) ^{4} | Ref. ^{5} | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Name | Acron. ^{1} | |||||||||||

Anthocyanin Reflectance Index | ARI | $\left(\frac{1}{{\mathrm{R}}_{550}}-\frac{1}{{\mathrm{R}}_{700}}\right){\mathrm{R}}_{770}$ | G | ant (L) | [80,81] | |||||||

Carotenoids Index green | Carg | $\left(\frac{1}{{\mathrm{R}}_{515}}-\frac{1}{{\mathrm{R}}_{565}}\right){\mathrm{R}}_{770}$ | G | car (L) | [80,82] | |||||||

Car red edge | Carre | $\left(\frac{1}{{\mathrm{R}}_{515}}-\frac{1}{{\mathrm{R}}_{700}}\right){\mathrm{R}}_{770}$ | G | car (L) | [80,82] | |||||||

Chlorophyll Index green | CIg | $\frac{{\mathrm{R}}_{780}}{{\mathrm{R}}_{550}}-1$ | G | chl (L) | [80,82] | |||||||

CI red edge | CIre | $\frac{{\mathrm{R}}_{780}}{{\mathrm{R}}_{710}}-1$ | A, G | chl (L) | [80,82] | |||||||

Chlorophyll Vegetation Index | CVI | $\frac{{\mathrm{R}}_{870}/{\mathrm{R}}_{550}}{{\mathrm{R}}_{670}/{\mathrm{R}}_{550}}$ | G | chl (L) | [83] | |||||||

Difference Vegetation index | DVI | ${\mathrm{R}}_{800}{-\mathrm{R}}_{680}$ | A, G | chl (L) | [84] | |||||||

Double Difference Index | DD | $\left({\mathrm{R}}_{749}{-\mathrm{R}}_{720}\right)-\left({\mathrm{R}}_{701}{-\mathrm{R}}_{672}\right)$ | A, G | chl (L) | [85] | |||||||

Greenness Index | GI | $\frac{{\mathrm{R}}_{554}}{{\mathrm{R}}_{677}}$ | G | chl, LAI, chl x LAI (L, C) | [86] | |||||||

Green Normalized Difference Vegetation Index | GNDVI1 to 3 | $\frac{{\mathrm{R}}_{875}{-\mathrm{R}}_{560}}{{\mathrm{R}}_{875}{+\mathrm{R}}_{560}};$ | $\frac{{\mathrm{R}}_{800}{-\mathrm{R}}_{550}}{{\mathrm{R}}_{800}{+\mathrm{R}}_{550}};$ | $\frac{{\mathrm{R}}_{750}{-\mathrm{R}}_{550}}{{\mathrm{R}}_{750}{+\mathrm{R}}_{550}}$ | G | chl, LAI, chl x LAI (L, C) | [87] | |||||

Greenness Vegetation Index | GVI | $\frac{{\mathrm{R}}_{682}{-\mathrm{R}}_{553}}{{\mathrm{R}}_{682}{+\mathrm{R}}_{553}}$ | G | chl, LAI, chl x LAI (L, C) | [88] | |||||||

Lichtenthaler Index | LIC | $\frac{{\mathrm{R}}_{800}{-\mathrm{R}}_{680}}{{\mathrm{R}}_{800}{+\mathrm{R}}_{680}}$ | A, G | chl, LAI, chl x LAI (L, C) | [89] | |||||||

Modified Chlorophyll Absorption in Reflectance Index | MCARI | $\left[\left({\mathrm{R}}_{700}{-\mathrm{R}}_{670}\right)-0.2\left({\mathrm{R}}_{700}{-\mathrm{R}}_{550}\right)\right]\left(\frac{{\mathrm{R}}_{700}}{{\mathrm{R}}_{670}}\right)$ | G | chl (L) | [90] | |||||||

MCARI red edge | MCARIre | $\left[\left({\mathrm{R}}_{750}{-\mathrm{R}}_{705}\right)-0.2\left({\mathrm{R}}_{750}{-\mathrm{R}}_{550}\right)\right]\left(\frac{{\mathrm{R}}_{750}}{{\mathrm{R}}_{705}}\right)$ | G | chl (L) | [91] | |||||||

– | MCARI2 | $\frac{1.5\left[2.5\left({\mathrm{R}}_{800}{-\mathrm{R}}_{670}\right)-1.3\left({\mathrm{R}}_{800}{-\mathrm{R}}_{550}\right)\right]}{\sqrt{{\left({2\mathrm{R}}_{800}+1\right)}^{2}-\left({6\mathrm{R}}_{800}-5\sqrt{{\mathrm{R}}_{670}}\right)-0.5}}$ | G | LAI (C) | [92] | |||||||

– | MCARI/ OSAVI | $\frac{\left[\left({\mathrm{R}}_{700}{-\mathrm{R}}_{670}\right)-0.2\left({\mathrm{R}}_{700}{-\mathrm{R}}_{550}\right)\right]\left(\frac{{\mathrm{R}}_{700}}{{\mathrm{R}}_{670}}\right)}{\left(1+0.16\right)\frac{\left({\mathrm{R}}_{800}{-\mathrm{R}}_{670}\right)}{\left({\mathrm{R}}_{800}{+\mathrm{R}}_{670}+0.16\right)}}$ | G | chl (L) | [90] | |||||||

MCARI/OSAVI red edge | MCARI/ OSAVIre | $\frac{\left[\left({\mathrm{R}}_{750}{-\mathrm{R}}_{705}\right)-0.2\left({\mathrm{R}}_{750}{-\mathrm{R}}_{550}\right)\right]\left(\frac{{\mathrm{R}}_{750}}{{\mathrm{R}}_{705}}\right)}{\left(1+0.16\right)\frac{\left({\mathrm{R}}_{750}{-\mathrm{R}}_{705}\right)}{\left({\mathrm{R}}_{750}{+\mathrm{R}}_{705}+0.16\right)}}$ | G | chl (L) | [91] | |||||||

– | Maccioni | $\frac{{\mathrm{R}}_{780}{-\mathrm{R}}_{710}}{{\mathrm{R}}_{780}{-\mathrm{R}}_{680}}$ | A, G | chl (L) | [93] | |||||||

Modified Simple Ratio | MSR1 and 2 | $\left(\frac{{\mathrm{R}}_{800}}{{\mathrm{R}}_{670}}-1\right)/{\left(\frac{{\mathrm{R}}_{800}}{{\mathrm{R}}_{670}}\right)}^{0.5}+1;$ | $\left(\frac{{\mathrm{R}}_{750}}{{\mathrm{R}}_{705}}-1\right)/{\left(\frac{{\mathrm{R}}_{750}}{{\mathrm{R}}_{705}}\right)}^{0.5}+1$ | A, G | chl (L) | [91,94] | ||||||

MERIS Terrestrial Chlorophyll Index | MTCI | $\frac{{\mathrm{R}}_{754}{-\mathrm{R}}_{709}}{{\mathrm{R}}_{709}{-\mathrm{R}}_{681}}$ | A, G | chl, LAI, chl x LAI (L, C) | [95] | |||||||

Modified Triangular Vegetation Index | MTVI | $1.2\left[1.2\left({\mathrm{R}}_{800}{-\mathrm{R}}_{550}\right)-2.5\left({\mathrm{R}}_{670}{-\mathrm{R}}_{550}\right)\right]$ | G | chl, LAI, chl x LAI (L, C) | [92] | |||||||

Normalized Difference Red Edge Index | NDRE | $\frac{{\mathrm{R}}_{790}{-\mathrm{R}}_{720}}{{\mathrm{R}}_{790}{+\mathrm{R}}_{720}}$ | A, G | chl (L) | [96] | |||||||

Normalized Difference Vegetation Index | NDVI | $\frac{{\mathrm{R}}_{800}{-\mathrm{R}}_{670}}{{\mathrm{R}}_{800}{+\mathrm{R}}_{670}}$ | A, G | chl, LAI, chl x LAI (L, C) | [97] | |||||||

NDVI red edge | NDVIre | $\frac{{\mathrm{R}}_{750}{-\mathrm{R}}_{705}}{{\mathrm{R}}_{750}{+\mathrm{R}}_{705}}$ | A, G | chl, LAI, chl x LAI (L, C) | [98] | |||||||

– | NDVI * SR | $\frac{{\mathrm{R}}_{800}^{2}{-\mathrm{R}}_{670}}{{\mathrm{R}}_{800}{+\mathrm{R}}_{670}^{2}}$ | A, G | LAI (C) | [99] | |||||||

Optimized Soil Adjusted Vegetation Index | OSAVI | $\left(1+0.16\right)\frac{\left({\mathrm{R}}_{800}{-\mathrm{R}}_{670}\right)}{\left({\mathrm{R}}_{800}{+\mathrm{R}}_{670}+0.16\right)}$ | A, G | chl, LAI, chl x LAI (L, C) | [100] | |||||||

OSAVI red edge | OSAVIre | $\left(1+0.16\right)\frac{\left({\mathrm{R}}_{750}{-\mathrm{R}}_{705}\right)}{\left({\mathrm{R}}_{750}{+\mathrm{R}}_{705}+0.16\right)}$ | A, G | chl, LAI, chl x LAI (L, C) | [91] | |||||||

Photochemical Reflectance Index | PRI | $\frac{{\mathrm{R}}_{570}{-\mathrm{R}}_{531}}{{\mathrm{R}}_{570}{+\mathrm{R}}_{531}}$ | G | xan, car, car/chl, LAI (L, C) | [101] | |||||||

Pigment Specific Normalized Difference | PSND | $\frac{{\mathrm{R}}_{800}{-\mathrm{R}}_{635}}{{\mathrm{R}}_{800}{+\mathrm{R}}_{635}}$ | A, G | chl, LAI, chl x LAI (L, C) | [102] | |||||||

Plant Senescence Reflectance Index | PSRI | $\frac{\left({\mathrm{R}}_{680}{-\mathrm{R}}_{500}\right)}{{\mathrm{R}}_{750}}$ | G | chl, car, car/chl (L) | [103] | |||||||

Pigment Specific Simple Ratio | PSSR1 and 2 | $\frac{{\mathrm{R}}_{800}}{{\mathrm{R}}_{650}};$ | $\frac{{\mathrm{R}}_{800}}{{\mathrm{R}}_{635}}$ | A, G | chl (L) | [102] | ||||||

– | PSSR3 | $\frac{{\mathrm{R}}_{800}}{{\mathrm{R}}_{500}}$ | G | car (L) | [102] | |||||||

Ratio Analysis of Reflectance Spectra | RARS1 and 2 | $\frac{{\mathrm{R}}_{675}}{{\mathrm{R}}_{700}};$ | $\frac{{\mathrm{R}}_{675}}{\left({\mathrm{R}}_{650}\times {\mathrm{R}}_{700}\right)}$ | A, G | chl (L) | [104] | ||||||

– | RARS3 | $\frac{{\mathrm{R}}_{760}}{{\mathrm{R}}_{500}}$ | G | car (L) | [104] | |||||||

Renormalized Difference Vegetation Index | RDVI | $\frac{{\mathrm{R}}_{800}{-\mathrm{R}}_{670}}{{\left({\mathrm{R}}_{800}{+\mathrm{R}}_{670}\right)}^{2}}$ | A, G | chl, LAI, chl x LAI (L, C) | [105] | |||||||

Red Edge Position | REP | $700+40\frac{\left[\left({\mathrm{R}}_{670}{+\mathrm{R}}_{780}\right)/2\right]{-\mathrm{R}}_{700}}{{\mathrm{R}}_{740}{-\mathrm{R}}_{700}}$ | A, G | chl, LAI, chl x LAI (L, C) | [106] | |||||||

Red Green Index | RGI | $\frac{{\mathrm{R}}_{690}}{{\mathrm{R}}_{550}}$ | G | car (L) | [86] | |||||||

Structure Insensitive Pigment Index | SIPI | $\frac{{\mathrm{R}}_{800}{-\mathrm{R}}_{450}}{{\mathrm{R}}_{800}{+\mathrm{R}}_{650}}$ | G | chl (L) | [107] | |||||||

Simple Ratio | SR1 | $\frac{{\mathrm{R}}_{752}}{{\mathrm{R}}_{690}}$ | A, G | chl (L) | [98,108] | |||||||

– | SR2 to 6 | $\frac{{\mathrm{R}}_{800}}{{\mathrm{R}}_{675}};$ | $\frac{{\mathrm{R}}_{750}}{{\mathrm{R}}_{700}};$ | $\frac{{\mathrm{R}}_{750}}{{\mathrm{R}}_{550}};$ | $\frac{{\mathrm{R}}_{700}}{{\mathrm{R}}_{670}};$ | $\frac{{\mathrm{R}}_{690}}{{\mathrm{R}}_{655}}$ | G | chl (L) | [84,98,108,109,110] | |||

Transformed Chlorophyll Absorption Ratio Index | TCARI | $3\left[\left({\mathrm{R}}_{700}{-\mathrm{R}}_{670}\right)-0.2\left({\mathrm{R}}_{700}{-\mathrm{R}}_{550}\right)\left(\frac{{\mathrm{R}}_{700}}{{\mathrm{R}}_{670}}\right)\right]$ | G | chl (L) | [111] | |||||||

TCARI red edge | TCARIre | $3\left[\left({\mathrm{R}}_{750}{-\mathrm{R}}_{705}\right)-0.2\left({\mathrm{R}}_{705}{-\mathrm{R}}_{550}\right)\left(\frac{{\mathrm{R}}_{750}}{{\mathrm{R}}_{705}}\right)\right]$ | G | chl (L) | [91] | |||||||

– | TCARI/ OSAVI | $\frac{3\left[\left({\mathrm{R}}_{700}{-\mathrm{R}}_{670}\right)-0.2\left({\mathrm{R}}_{700}{-\mathrm{R}}_{550}\right)\left(\frac{{\mathrm{R}}_{700}}{{\mathrm{R}}_{670}}\right)\right]}{\left(1+0.16\right)\frac{\left({\mathrm{R}}_{800}{-\mathrm{R}}_{670}\right)}{\left({\mathrm{R}}_{800}{+\mathrm{R}}_{670}+0.16\right)}}$ | G | chl (L) | [111] | |||||||

TCARI/OSAVI red edge | TCARI/ OSAVIre | $\frac{3\left[\left({\mathrm{R}}_{750}{-\mathrm{R}}_{705}\right)-0.2\left({\mathrm{R}}_{750}{-\mathrm{R}}_{550}\right)\left(\frac{{\mathrm{R}}_{750}}{{\mathrm{R}}_{705}}\right)\right]}{\left(1+0.16\right)\frac{\left({\mathrm{R}}_{750}{-\mathrm{R}}_{705}\right)}{\left({\mathrm{R}}_{750}{+\mathrm{R}}_{705}+0.16\right)}}$ | G | chl (L) | [91] | |||||||

Triangular Chlorophyll Index | TCI | $1.2\left({\mathrm{R}}_{700}/{\mathrm{R}}_{550}\right)-1.5\left({\mathrm{R}}_{670}/{\mathrm{R}}_{550}\right)\times \sqrt{{\mathrm{R}}_{700}/{\mathrm{R}}_{670}}$ | G | chl (L) | [112] | |||||||

– | TCI/OSAVI | $\frac{1.2\left({\mathrm{R}}_{700}/{\mathrm{R}}_{550}\right)-1.5\left({\mathrm{R}}_{670}/{\mathrm{R}}_{550}\right)\times \sqrt{{\mathrm{R}}_{700}/{\mathrm{R}}_{670}}}{\left(1+0.16\right)\frac{\left({\mathrm{R}}_{800}{-\mathrm{R}}_{670}\right)}{\left({\mathrm{R}}_{800}{+\mathrm{R}}_{670}+0.16\right)}}$ | G | chl (L) | [112] | |||||||

Triangular Vegetation Index | TVI | $0.5\left[120\left({\mathrm{R}}_{750}{-\mathrm{R}}_{550}\right)-200\left({\mathrm{R}}_{670}{-\mathrm{R}}_{550}\right)\right]$ | G | chl, LAI, chl x LAI (L, C) | [113] | |||||||

Weighted Difference Vegetation Index | WDVI | ${R}_{870}\text{}-\text{}\left(C\text{}\times \text{}{R}_{670}\right);$ $\mathrm{C}=\frac{{\mathrm{RSoil}}_{870}}{{\mathrm{RSoil}}_{670}}$ | A, G | LAI (C) | [114] |

^{1}Acron. = Acronyms for VIs names;

^{2}Rw = reflectance in the spectral band centered in w, RSoilw = reflectance of bare soil in the spectral band centered in w.

^{3}Acquisition (Acq.) level of the data used for calculation: airborne (A) or ground-based (G);

^{4}chl = leaf chlorophylls content, LAI = leaf area index, chl x LAI = canopy chlorophylls content, xan = xantophylls, car = carotenoids, car/chl = ratio between carotenoids and chlorophylls, L = leaf scale, C = canopy scale;

^{5}References in the literature (Ref.) for the VIs formulations.

DAP | Pixels Labelled as Vegetation in the Training Data (%) | Retained Vegetation Indices (Table A2) Used for Binary Classification after Regularization | Ground Cover Estimates after Image Clustering | |||
---|---|---|---|---|---|---|

Calibration Dataset | Test Dataset | Percentile (%) of Probability Estimate (for Cluster-Wise Class Assignment) | Root Mean Squared Error (% of Ground Cover) | |||

Calibration Dataset | Test Dataset | |||||

37 | 29.1 | 20.7 | CVI, PSSR1, PSSR2, PSSR3, RARS3, REP, SR1, SR3, TVI | 37.5 | 2.07 | 2.56 |

50 | 73.0 | 64.8 | CVI, PSSR1, PSSR2, PSSR3, RARS2, RARS3, REP, SR1, SR3, TVI | 59.0 | 2.41 | 2.09 |

64 | 67.1 | 92.0 | CVI, PSSR1, RARS2, RDVI, REP, SR1, TVI | 60.0 | 3.51 | 2.48 |

78 | 82.1 | 81.8 | CVI, MTCI, PSSR1, PSSR2, PSSR3, RARS2, RARS3, REP, SR1, TVI | 85.0 | 1.56 | 2.95 |

**Figure A1.**RMSE for ground cover retrieval at SU level by applying vegetation index (VI) threshold to the UAV images. Values after VI labels in the graph indicate the median RMSE for the respective index, considering the validation dataset (i.e., four sampling units per acquisition date).

**Table A4.**Vegetation indices (VIs; Table A2) thresholds obtained after optimization for background removal in UAV images. Different values were derived for each acquisition date in order to adapt background removal to crop development and measurement conditions. Results are ordered following the segmentation performance presented in Figure A1.

VI | 37 DAP | 50 DAP | 64 DAP | 78 DAP |
---|---|---|---|---|

NDVI*SR | 0.064 | 0.287 | 0.204 | 0.192 |

OSAVI | 0.493 | 0.679 | 0.647 | 0.623 |

DD | 0.057 | 0.107 | 0.085 | 0.067 |

DVI | 0.202 | 0.342 | 0.259 | 0.269 |

WDVI | 0.182 | 0.340 | 0.252 | 0.253 |

NDVI | 0.620 | 0.779 | 0.828 | 0.783 |

PSSR1 | 4.269 | 8.088 | 10.630 | 8.245 |

MSR1 | 2.582 | 3.490 | 3.952 | 3.520 |

PSND | 0.616 | 0.764 | 0.802 | 0.738 |

PSSR2 | 4.214 | 7.491 | 9.114 | 6.637 |

SR1 | 3.384 | 5.893 | 7.863 | 6.186 |

LIC | 0.563 | 0.722 | 0.783 | 0.730 |

OSAVIre | 0.247 | 0.393 | 0.364 | 0.288 |

MTCI | 2.234 | 2.640 | 2.348 | 1.414 |

REP | 712.885 | 719.069 | 720.956 | 718.168 |

Maccioni | 0.717 | 0.740 | 0.712 | 0.601 |

RARS1 | 0.826 | 0.794 | 0.784 | 0.782 |

CIre | 0.925 | 1.673 | 1.694 | 1.052 |

NDRE | 0.316 | 0.455 | 0.458 | 0.345 |

MSR2 | 1.621 | 1.967 | 1.989 | 1.701 |

NDVIre | 0.296 | 0.435 | 0.443 | 0.331 |

RDVI | 1.410 | 1.803 | 2.598 | 2.111 |

RARS2 | 10.172 | 16.311 | 29.685 | 20.889 |

**Figure A2.**Archetypes derived for ground-based (

**b**) and UAV (

**e**) images acquired 78 DAP (color coded from green to red according to average reflectance in the NIR). In the same graphs (

**a**,

**e**) spectra for two pixels selected from a UAV image patch corresponding to T2 (mixed system) are also described (green and red dashed lines with dots). The weighting for reconstruction of these spectra based on the archetypes are described in the radar plots for the ground-based (

**c**) and UAV (

**f**) data. The areas (green and red squares) corresponding to the selected UAV pixels (

**d**) in the ground-based image (

**a**) had their spectra and weights extracted and averaged to represent comparable information to that obtained for UAV data. Colors on (

**a**) and (

**d**) indicate values of OSAVI (Vegetation Index, Table A2; VI) for the segmented vegetation.

**Table A5.**Distribution of scores given at sampling unit level during disease assessment. Number of sampling units (SUs) assigned to a given class are indicated for each acquisition date, together with the number of imaged SUs using the spectral sensor in handheld mode (in parentheses). Numbers in red indicate SUs not used for evaluating the effects of different disease severity classes on the crop spectral response (see Section 2.8).

Treat. ^{1} | Disease Severity Class (%) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

0 | ≤1 | ≤2.5 | ≤5 | ≤7 | ≤10 | ≤15 | ≤25 | ≤50 | ≤75 | ≤90 | ≤97.5 | >97.5 | |

37 DAP | |||||||||||||

I | 20 (4) | – | – | – | – | – | – | – | – | – | – | – | – |

II | 20 (4) | – | – | – | – | – | – | – | – | – | – | – | – |

50 DAP | |||||||||||||

I | 20 (4) | – | – | – | – | – | – | – | – | – | – | – | – |

II | 20 (4) | – | – | – | – | – | – | – | – | – | – | – | – |

64 DAP | |||||||||||||

I | 28 (3) | 16 (1) | – | – | – | – | – | – | – | – | – | – | – |

II | 35 (4) | 9 | – | – | – | – | – | – | – | – | – | – | – |

70 DAP | |||||||||||||

I | 9 | 32 | 3 | – | – | – | – | – | – | – | – | – | – |

II | 32 | 12 | – | – | – | – | – | – | – | – | – | – | – |

73 DAP | |||||||||||||

I | – | – | 14 | 11 | 9 | 6 | – | – | – | – | – | – | – |

II | 5 | 14 | 16 | 5 | – | – | – | – | – | – | – | – | – |

78 DAP | |||||||||||||

I | – | – | – | 2 | 16 (1) | 11 (1) | 14 (2) | – | 1 | – | – | – | – |

II | 2 | 8 (1) | 24 (1) | 8 (2) | 2 | – | – | – | – | – | – | – | – |

83 DAP | |||||||||||||

I | – | – | – | – | – | – | 2 | 2 | 9 | 11 | 19 | 1 | – |

II | – | – | 1 | 1 | 1 | 4 | 9 | 13 | 12 | 3 | – | – | – |

86 DAP | |||||||||||||

I | – | – | – | – | – | – | – | – | – | – | 8 | 29 | – |

II | – | – | – | – | – | – | – | 1 | 39 | – | – | – | – |

^{1}Treat. I—plots with one single cultivar (“non-mixed”); Treat. II—plots with a mix of cultivars (“mixed”).

**Table A6.**C-statistic for pixel-wise binary classification according to T1 (“non-mixed” system) or T2 (“mixed” system) in each acquisition date using vegetation indices (VIs; Table A2) as independent variables. Only results concerning UAV-derived spectra and sampling units selected for validation of the logistic regressions are reported. Results are ordered (parentheses) according to values of C-statistic for the last acquisition (78 DAP).

VI | 37 DAP | 50 DAP | 64 DAP | 78 DAP |
---|---|---|---|---|

Group I—VIs optimized to estimate leaf chlorophyll content | ||||

MSR2 | 0.642 (7) | 0.516 (12) | 0.601 (4) | 0.761 (1) |

SR1 | 0.716 (3) | 0.528 (9) | 0.605 (1) | 0.756 (2) |

CIre | 0.62 (9) | 0.523 (10) | 0.604 (3) | 0.752 (3) |

NDRE | 0.62 (10) | 0.523 (11) | 0.604 (2) | 0.752 (4) |

PSSR1 | 0.712 (5) | 0.555 (7) | 0.598 (5) | 0.739 (6) |

MSR1 | 0.712 (4) | 0.555 (6) | 0.598 (6) | 0.739 (5) |

MAC | 0.565 (12) | 0.564 (5) | 0.588 (7) | 0.728 (7) |

PSSR2 | 0.752 (2) | 0.549 (8) | 0.559 (8) | 0.713 (8) |

DD | 0.596 (11) | 0.567 (4) | 0.507 (11) | 0.617 (9) |

DVI | 0.641 (8) | 0.571 (3) | 0.497 (12) | 0.608 (10) |

RARS2 | 0.803 (1) | 0.584 (2) | 0.553 (9) | 0.604 (11) |

RARS1 | 0.67 (6) | 0.608 (1) | 0.52 (10) | 0.527 (12) |

Group II—VIs optimized to estimate canopy traits | ||||

REP | 0.693 (5) | 0.513 (11) | 0.632 (1) | 0.767 (1) |

NDVIre | 0.642 (6) | 0.516 (10) | 0.601 (3) | 0.761 (2) |

LIC | 0.71 (4) | 0.525 (9) | 0.61 (2) | 0.755 (3) |

OSAVIre | 0.533 (10) | 0.533 (8) | 0.565 (6) | 0.749 (4) |

MTCI | 0.529 (11) | 0.558 (5) | 0.584 (5) | 0.739 (5) |

NDVI | 0.712 (3) | 0.555 (6) | 0.598 (4) | 0.739 (6) |

PSND | 0.752 (2) | 0.549 (7) | 0.559 (7) | 0.713 (7) |

OSAVI | 0.545 (9) | 0.573 (4) | 0.515 (9) | 0.654 (8) |

NDVI*SR | 0.576 (8) | 0.574 (1) | 0.495 (11) | 0.619 (9) |

WDVI | 0.62 (7) | 0.573 (3) | 0.508 (10) | 0.614 (10) |

RDVI | 0.789 (1) | 0.573 (2) | 0.517 (8) | 0.538 (11) |

**Figure A3.**Linear regression (fitted by ordinary least squares) between crop traits and vegetation indices (VIs = CIre, REP, WDVI, and OSAVI;

**a**–

**d**) and between WDVI and other VIs (

**e**). Prediction and confidence intervals (95%) are presented in blue dashed lines. Colors from green to red indicate time of acquisition (from 37 to 78 DAS). Dots and triangles correspond to the non-mixed and mixed cropping system, respectively. Only the last three acquisitions are taken into account for evaluating the relationship between traits and VIS (

**a**–

**d**), all the data are considered for the comparison between WDVI and other VIs (

**e**).

**Figure A4.**Ground-based (

**a**,

**c**,

**e**,

**g**) and UAV (

**b**,

**d**,

**f**,

**h**) imagery for SUs over the growing season. SUs cultivated with T1 (“non-mixed”) are represented in red frames and images corresponding to T2 (“mixed”) in black frames. False color composites (828, 660, and 607 nm as RGB for ground-based and nearest bands for UAV images) are displayed on the background and foreground shows OSAVI (VI) after vegetation segmentation. Scale bars (left upper corners) indicate 25 cm. For 64 DAP, frames in dashed lines indicate SUs not measured during other acquisitions.

**Figure A5.**Linear regression (fitted by ordinary least squares) between ground-based and UAV data (OSAVI,

**a**; LLR,

**b**) corresponding to the median values for eight SUs followed during the growing season. Prediction and confidence intervals (95%) are presented in blue dashed lines. Red dashed line indicate the 1:1 diagonal line. Dots correspond to the non-mixed treatment and triangles to the mixed cropping system. Colors from green to red indicate time of acquisition (from 37 to 78 DAS).

**Table A7.**Kendall-tau correlation coefficients between disease severity classes (as ordinal variable) and median of vegetation indices (VIs; as continuous variable) for UAV data at sampling unit level for assessment made 64 and 78 DAP.

Dataset | CIre ^{1} | REP ^{1} | WDVI ^{1} |
---|---|---|---|

64 DAP ^{2} | |||

All pixels | −0.172 | −0.151 | −0.132 |

Upper 20th percentile of VI values | −0.147 | −0.144 | −0.071 |

Upper 10th percentile of VI values | −0.147 | −0.141 | −0.098 |

78 DAP ^{2} | |||

All pixels | −0.534 *** | −0.549 *** | −0.479 *** |

Upper 20th percentile of VI values | −0.509 *** | −0.518 *** | −0.461 *** |

Upper 10th percentile of VI values | −0.486 *** | −0.499 *** | −0.464 *** |

^{1}Significant at 0.05 (*), 0.01 (**) or 0.001 (***) level;

^{2}only observations from T1 considered for 64 DAP while data corresponding to both treatments were used for 78 DAP.

**Table A8.**C-statistic for pixel-wise binary classification according to two specific disease severity (DS) classes (DS between 2.5 and 5.0% and between 10.0 and 15.0%) in contrast to a healthier reference (DS up to 1.0%), for the last acquisition date (78 DAP). Only results concerning UAV-acquired spectra and sampling units (SUs) selected for validation of the classification approach are reported. Results are ordered (parentheses) according to values of C-statistic for all pixels within the SUs considered. Results concerning the selection of pixels within the upper 20th and 10th percentiles of log-likelihood ratio (LLR) values indicating association with a given DS class are also presented.

Vegetation Index | All Data | 20th Percentile of LLR | 10th Percentile of LLR | |||
---|---|---|---|---|---|---|

DS ≤ 5.0% | DS ≤ 15.0% | DS ≤ 5.0% | DS ≤ 15.0% | DS ≤ 5.0% | DS ≤ 15.0% | |

Group I—leaf chlorophyll content related | ||||||

MSR2 | 0.583 (5) | 0.839 (1) | 0.550 (7) | 0.936 (1) | 0.568 (4) | 0.944 (3) |

NDRE | 0.546 (10) | 0.828 (2) | 0.450 (12) | 0.928 (3) | 0.431 (12) | 0.948 (1) |

CIre | 0.552 (9) | 0.828 (3) | 0.466 (11) | 0.933 (2) | 0.433 (11) | 0.945 (2) |

MAC | 0.598 (3) | 0.802 (4) | 0.540 (9) | 0.912 (4) | 0.441 (10) | 0.943 (4) |

SR1 | 0.574 (6) | 0.802 (5) | 0.583 (5) | 0.844 (5) | 0.529 (7) | 0.786 (5) |

PSSR1 | 0.574 (7) | 0.771 (6) | 0.568 (6) | 0.803 (8) | 0.521 (8) | 0.746 (6) |

MSR1 | 0.572 (8) | 0.769 (7) | 0.584 (4) | 0.820 (6) | 0.513 (9) | 0.744 (7) |

DD | 0.609 (2) | 0.756 (8) | 0.693 (2) | 0.703 (10) | 0.616 (2) | 0.722 (9) |

DVI | 0.622 (1) | 0.726 (9) | 0.715 (1) | 0.806 (7) | 0.641 (1) | 0.562 (12) |

PSSR2 | 0.494 (11) | 0.72 (10) | 0.548 (8) | 0.763 (9) | 0.534 (6) | 0.733 (8) |

RARS2 | 0.592 (4) | 0.509 (11) | 0.654 (3) | 0.572 (11) | 0.583 (3) | 0.669 (10) |

RARS1 | 0.482 (12) | 0.484 (12) | 0.537 (10) | 0.556 (12) | 0.546 (5) | 0.603 (11) |

Group II—canopy traits related | ||||||

OSAVIre | 0.495 (10) | 0.849 (1) | 0.567 (8) | 0.971 (1) | 0.503 (11) | 0.962 (1) |

REP | 0.518 (9) | 0.837 (2) | 0.532 (11) | 0.901 (4) | 0.518 (8) | 0.883 (4) |

NDVIre | 0.582 (6) | 0.837 (3) | 0.556 (9) | 0.939 (2) | 0.548 (7) | 0.952 (2) |

MTCI | 0.637 (1) | 0.812 (4) | 0.606 (5) | 0.929 (3) | 0.576 (5) | 0.942 (3) |

LIC | 0.580 (7) | 0.804 (5) | 0.598 (6) | 0.855 (5) | 0.572 (6) | 0.804 (5) |

NDVI | 0.572 (8) | 0.768 (6) | 0.574 (7) | 0.806 (6) | 0.508 (10) | 0.742 (6) |

OSAVI | 0.615 (4) | 0.752 (7) | 0.692 (4) | 0.752 (8) | 0.656 (2) | 0.631 (8) |

WDVI | 0.627 (2) | 0.747 (8) | 0.746 (1) | 0.722 (9) | 0.708 (1) | 0.589 (9) |

NDVI*SR | 0.614 (5) | 0.732 (9) | 0.700 (3) | 0.721 (10) | 0.649 (3) | 0.580 (10) |

PSND | 0.484 (11) | 0.721 (10) | 0.547 (10) | 0.768 (7) | 0.515 (9) | 0.730 (7) |

RDVI | 0.615 (3) | 0.669 (11) | 0.719 (2) | 0.609 (11) | 0.632 (4) | 0.517 (11) |

**Figure A6.**Log-likelihood ratio (LLR) for ground-based (

**a**,

**c**,

**e**,

**g**) and UAV imagery (

**b**,

**d**,

**f**,

**h**). LLR, in this case, indicates the comparison of pixel-wise probability estimated for T1 (H1; “non-mixed” system) in contrast to T2 (H0; “mixed” system). SUs cultivated with T1 are represented with red frames and scale bars in the left upper corner of each image correspond to 25 cm. For 64 DAP, frames represented in dashed lines indicate SUs not measured during other acquisitions and which cannot be compared over time.

**Figure A7.**Imaged patch relatively highly affected by late blight 78 DAP (i.e., first sampling unit represented in Figure A6g). Image (

**a**,

**b**) corresponds to false color composite for ground image after background removal (620, 542, and 503 nm as RGB bands) and image (

**c**) indicates log-likelihood ratio for pixels in the highlighted area in (a, red square), also depicted in (

**b**).

## References

- Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng.
**2013**, 114, 358–371. [Google Scholar] [CrossRef] - Kuska, M.T.; Mahlein, A.-K. Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors. Eur. J. Plant Pathol.
**2018**, 152, 987–992. [Google Scholar] [CrossRef] - Aasen, H.; Bolten, A. Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers—From theory to application. Remote Sens. Environ.
**2018**, 205, 374–389. [Google Scholar] [CrossRef] - Capolupo, A.; Kooistra, L.; Berendonk, C.; Boccia, L.; Suomalainen, J. Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches. ISPRS Int. J. Geo-Inf.
**2015**, 4, 2792–2820. [Google Scholar] [CrossRef] [Green Version] - Roosjen, P.P.J.; Brede, B.; Suomalainen, J.M.; Bartholomeus, H.M.; Kooistra, L.; Clevers, J.G.P.W. Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data—potential of unmanned aerial vehicle imagery. Int. J. Appl. Earth Obs. Geoinf.
**2018**, 66, 14–26. [Google Scholar] [CrossRef] - Bock, C.H.; Poole, G.H.; Parker, P.E.; Gottwald, T.R. Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging. Crit. Rev. Plant Sci.
**2010**, 29, 59–107. [Google Scholar] [CrossRef] - Kuska, M.T.; Brugger, A.; Thomas, S.; Wahabzada, M.; Kersting, K.; Oerke, E.-C.; Steiner, U.; Mahlein, A.-K. Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei. Phytopathology
**2017**, 107, 1388–1398. [Google Scholar] [CrossRef] [PubMed] - Wahabzada, M.; Mahlein, A.-K.; Bauckhage, C.; Steiner, U.; Oerke, E.-C.; Kersting, K. Metro Maps of Plant Disease Dynamics—Automated Mining of Differences Using Hyperspectral Images. PLoS ONE
**2015**, 10, e0116902. [Google Scholar] [CrossRef] - Wahabzada, M.; Mahlein, A.-K.; Bauckhage, C.; Steiner, U.; Oerke, E.-C.; Kersting, K. Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants. Sci. Rep.
**2016**, 6, srep22482. [Google Scholar] [CrossRef] - Mahlein, A.-K.; Rumpf, T.; Welke, P.; Dehne, H.-W.; Plümer, L.; Steiner, U.; Oerke, E.-C. Development of spectral indices for detecting and identifying plant diseases. Remote Sens. Environ.
**2013**, 128, 21–30. [Google Scholar] [CrossRef] - Thomas, S.; Behmann, J.; Steier, A.; Kraska, T.; Muller, O.; Rascher, U.; Mahlein, A.-K. Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. Plant Methods
**2018**, 14, 45. [Google Scholar] [CrossRef] [PubMed] - Behmann, J.; Acebron, K.; Emin, D.; Bennertz, S.; Matsubara, S.; Thomas, S.; Bohnenkamp, D.; Kuska, M.T.; Jussila, J.; Salo, H.; et al. Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection. Sensors
**2018**, 18, 441. [Google Scholar] [CrossRef] [PubMed] - Garcia-Ruiz, F.; Sankaran, S.; Maja, J.M.; Lee, W.S.; Rasmussen, J.; Ehsani, R. Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Comput. Electron. Agric.
**2013**, 91, 106–115. [Google Scholar] [CrossRef] - Calderón, R.; Navas-Cortés, J.; Zarco-Tejada, P. Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas. Remote Sens.
**2015**, 7, 5584–5610. [Google Scholar] [CrossRef] [Green Version] - Thomas, S.; Kuska, M.T.; Bohnenkamp, D.; Brugger, A.; Alisaac, E.; Wahabzada, M.; Behmann, J.; Mahlein, A.-K. Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective. J. Plant Dis. Prot.
**2017**, 125, 5–20. [Google Scholar] [CrossRef] - Behmann, J.; Steinrücken, J.; Plümer, L. Detection of early plant stress responses in hyperspectral images. ISPRS J. Photogramm. Remote Sens.
**2014**, 93, 98–111. [Google Scholar] [CrossRef] - Albetis, J.; Duthoit, S.; Guttler, F.; Jacquin, A.; Goulard, M.; Poilvé, H.; Féret, J.-B.; Dedieu, G. Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sens.
**2017**, 9, 308. [Google Scholar] [CrossRef] - Calderón, R.; Navas-Cortés, J.A.; Lucena, C.; Zarco-Tejada, P.J. High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sens. Environ.
**2013**, 139, 231–245. [Google Scholar] [CrossRef] [Green Version] - Li, X.; Lee, W.S.; Li, M.; Ehsani, R.; Mishra, A.R.; Yang, C.; Mangan, R.L. Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Comput. Electron. Agric.
**2012**, 83, 32–46. [Google Scholar] [CrossRef] - López-López, M.; Calderón, R.; González-Dugo, V.; Zarco-Tejada, P.J.; Fereres, E. Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery. Remote Sens.
**2016**, 8, 276. [Google Scholar] [CrossRef] - MacDonald, S.L.; Staid, M.; Staid, M.; Cooper, M.L. Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards. Comput. Electron. Agric.
**2016**, 130, 109–117. [Google Scholar] [CrossRef] - Zarco-Tejada, P.J.; Camino, C.; Beck, P.S.A.; Calderon, R.; Hornero, A.; Hernández-Clemente, R.; Kattenborn, T.; Montes-Borrego, M.; Susca, L.; Morelli, M.; et al. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants
**2018**, 4, 432–439. [Google Scholar] [CrossRef] [PubMed] - Huang, W.; Lamb, D.W.; Niu, Z.; Zhang, Y.; Liu, L.; Wang, J. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precis. Agric.
**2007**, 8, 187–197. [Google Scholar] [CrossRef] - Mewes, T.; Franke, J.; Menz, G. Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection. Precis. Agric.
**2011**, 12, 795–812. [Google Scholar] [CrossRef] - Calderón, R.; Montes-Borrego, M.; Landa, B.B.; Navas-Cortés, J.A.; Zarco-Tejada, P.J. Detection of downy mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle. Precis. Agric.
**2014**, 15, 639–661. [Google Scholar] [CrossRef] [Green Version] - Sankaran, S.; Maja, J.; Buchanon, S.; Ehsani, R. Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques. Sensors
**2013**, 13, 2117–2130. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Jansen, M.; Bergsträsser, S.; Schmittgen, S.; Müller-Linow, M.; Rascher, U. Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding. Agriculture
**2014**, 4, 147–158. [Google Scholar] [CrossRef] [Green Version] - Polder, G.; van der Heijden, G.W.A.M.; van Doorn, J.; Baltissen, T.A.H.M.C. Automatic detection of tulip breaking virus (TBV) in tulip fields using machine vision. Biosyst. Eng.
**2014**, 117, 35–42. [Google Scholar] [CrossRef] [Green Version] - Whetton, R.L.; Hassall, K.L.; Waine, T.W.; Mouazen, A.M. Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement. Biosyst. Eng.
**2018**, 166, 101–115. [Google Scholar] [CrossRef] - Moshou, D.; Bravo, C.; Oberti, R.; West, J.S.; Ramon, H.; Vougioukas, S.; Bochtis, D. Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosyst. Eng.
**2011**, 108, 311–321. [Google Scholar] [CrossRef] - Schaepman-Strub, G.; Schaepman, M.E.; Painter, T.H.; Dangel, S.; Martonchik, J.V. Reflectance quantities in optical remote sensing—Definitions and case studies. Remote Sens. Environ.
**2006**, 103, 27–42. [Google Scholar] [CrossRef] - Herrmann, I.; Vosberg, S.K.; Ravindran, P.; Singh, A.; Chang, H.-X.; Chilvers, M.I.; Conley, S.P.; Townsend, P.A. Leaf and Canopy Level Detection of Fusarium Virguliforme (Sudden Death Syndrome) in Soybean. Remote Sens.
**2018**, 10, 426. [Google Scholar] [CrossRef] - Zhang, J.; Pu, R.; Huang, W.; Yuan, L.; Luo, J.; Wang, J. Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Field Crop. Res.
**2012**, 134, 165–174. [Google Scholar] [CrossRef] - Yu, K.; Leufen, G.; Hunsche, M.; Noga, G.; Chen, X.; Bareth, G. Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices. Remote Sens.
**2013**, 6, 64–86. [Google Scholar] [CrossRef] [Green Version] - Yang, C.-M. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precis. Agric.
**2009**, 11, 61–81. [Google Scholar] [CrossRef] - Rumpf, T.; Mahlein, A.-K.; Steiner, U.; Oerke, E.-C.; Dehne, H.-W.; Plümer, L. Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Comput. Electron. Agric.
**2010**, 74, 91–99. [Google Scholar] [CrossRef] - Cooke, L.R.; Schepers, H.T.A.M.; Hermansen, A.; Bain, R.A.; Bradshaw, N.J.; Ritchie, F.; Shaw, D.S.; Evenhuis, A.; Kessel, G.J.T.; Wander, J.G.N.; et al. Epidemiology and Integrated Control of Potato Late Blight in Europe. Potato Res.
**2011**, 54, 183–222. [Google Scholar] [CrossRef] [Green Version] - Sugiura, R.; Tsuda, S.; Tamiya, S.; Itoh, A.; Nishiwaki, K.; Murakami, N.; Shibuya, Y.; Hirafuji, M.; Nuske, S. Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosyst. Eng.
**2016**, 148, 1–10. [Google Scholar] [CrossRef] - Duarte-Carvajalino, J.; Alzate, D.; Ramirez, A.; Santa-Sepulveda, J.; Fajardo-Rojas, A.; Soto-Suárez, M. Evaluating Late Blight Severity in Potato Crops Using Unmanned Aerial Vehicles and Machine Learning Algorithms. Remote Sens.
**2018**, 10, 1513. [Google Scholar] [CrossRef] - Nebiker, S.; Lack, N.; Abächerli, M.; Läderach, S. Light-weight multispectral UAV sensors and their capabilities for predicting grain yield and detecting plant diseases. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2016**, XLI-B1, 963–970. [Google Scholar] [CrossRef] - Whitehead, K.; Hugenholtz, C.H.; Myshak, S.; Brown, O.; LeClair, A.; Tamminga, A.; Barchyn, T.E.; Moorman, B.; Eaton, B. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 2: Scientific and commercial applications. J. Unmanned Veh. Sys.
**2014**, 2, 86–102. [Google Scholar] [CrossRef] - Franceschini, M.H.D.; Bartholomeus, H.; van Apeldoorn, D.; Suomalainen, J.; Kooistra, L. Intercomparison of Unmanned Aerial Vehicle and Ground-Based Narrow Band Spectrometers Applied to Crop Trait Monitoring in Organic Potato Production. Sensors
**2017**, 17, 1428. [Google Scholar] [CrossRef] - Franceschini, M.H.D.; Bartholomeus, H.; van Apeldoorn, D.; Suomalainen, J.; Kooistra, L. Assessing Changes in Potato Canopy Caused by Late Blight in Organic Production Systems Through Uav-Based Pushbroom Imaging Spectrometer. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2017**, 109–112. [Google Scholar] [CrossRef] - Römer, C.; Wahabzada, M.; Ballvora, A.; Pinto, F.; Rossini, M.; Panigada, C.; Behmann, J.; Léon, J.; Thurau, C.; Bauckhage, C.; et al. Early drought stress detection in cereals: Simplex volume maximisation for hyperspectral image analysis. Funct. Plant Biol.
**2012**, 39, 878. [Google Scholar] [CrossRef] - European and Mediterranean Plant Protection Organization Phytophthora infestans on potato. EPPO Bull.
**2008**, 38, 268–271. [CrossRef] - Honkavaara, E.; Saari, H.; Kaivosoja, J.; Pölönen, I.; Hakala, T.; Litkey, P.; Mäkynen, J.; Pesonen, L. Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture. Remote Sens.
**2013**, 5, 5006–5039. [Google Scholar] [CrossRef] [Green Version] - Clevers, J.G.P.W.; Kooistra, L. Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.
**2012**, 5, 574–583. [Google Scholar] [CrossRef] - Harwin, S.; Lucieer, A.; Osborn, J. The Impact of the Calibration Method on the Accuracy of Point Clouds Derived Using Unmanned Aerial Vehicle Multi-View Stereopsis. Remote Sens.
**2015**, 7, 11933–11953. [Google Scholar] [CrossRef] [Green Version] - Honkavaara, E.; Rosnell, T.; Oliveira, R.; Tommaselli, A. Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes. ISPRS J. Photogramm. Remote Sens.
**2017**, 134, 96–109. [Google Scholar] [CrossRef] - Meier, U. Growth stages of mono- and dicotyledonous plants: BBCH Monograph. OpenAgrar Repository
**2018**. [Google Scholar] [CrossRef] - Zhang, Z. A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell.
**2000**, 22, 1330–1334. [Google Scholar] [CrossRef] [Green Version] - Lowekamp, B.C.; Chen, D.T.; Ibanez, L.; Blezek, D. The Design of SimpleITK. Front. Neuroinform.
**2013**, 7, 45. [Google Scholar] [CrossRef] [PubMed] - Vakalopoulou, M.; Karantzalos, K. Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data. Remote Sens.
**2014**, 6, 3409–3426. [Google Scholar] [CrossRef] [Green Version] - Mattes, D.; Haynor, D.R.; Vesselle, H.; Lewellen, T.K.; Eubank, W. Nonrigid multimodality image registration. Med. Imaging
**2001**, 1609–1620. [Google Scholar] - Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis.
**2004**, 60, 91–110. [Google Scholar] [CrossRef] [Green Version] - Muja, M.; Lowe, D.G. Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP
**2009**, 2, 331–340. [Google Scholar] - Tibshirani, R.; Walther, G.; Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B (Stat. Methodol.)
**2001**, 63, 411–423. [Google Scholar] [CrossRef] [Green Version] - Zou, H.; Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.)
**2005**, 67, 301–320. [Google Scholar] [CrossRef] - Jay, S.; Gorretta, N.; Morel, J.; Maupas, F.; Bendoula, R.; Rabatel, G.; Dutartre, D.; Comar, A.; Baret, F. Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery. Remote Sens. Environ.
**2017**, 198, 173–186. [Google Scholar] [CrossRef] - Wood, C.W.; Reeves, D.W.; Duffield, R.R.; Edmisten, K.L. Field chlorophyll measurements for evaluation of corn nitrogen status. J. Plant Nutr.
**1992**, 15, 487–500. [Google Scholar] [CrossRef] - Uddling, J.; Gelang-Alfredsson, J.; Piikki, K.; Pleijel, H. Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth. Res.
**2007**, 91, 37–46. [Google Scholar] [CrossRef] [PubMed] - Clevers, J.G.P.W. Beyond NDVI: Extraction of Biophysical Variables from Remote Sensing Imagery. In Land Use and Land Cover Mapping in Europe; Manakos, I., Braun, M., Eds.; Springer: Dordrecht, The Netherlands, 2014; Volume 18, pp. 363–381. ISBN 978-94-007-7968-6. [Google Scholar]
- Yu, K.; Anderegg, J.; Mikaberidze, A.; Karisto, P.; Mascher, F.; McDonald, B.A.; Walter, A.; Hund, A. Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease. Front. Plant Sci.
**2018**, 9, 1195. [Google Scholar] [CrossRef] [PubMed] - Delalieux, S.; Auwerkerken, A.; Verstraeten, W.W.; Somers, B.; Valcke, R.; Lhermitte, S.; Keulemans, J.; Coppin, P. Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves. Remote Sens.
**2009**, 1, 858–874. [Google Scholar] [CrossRef] [Green Version] - Gałecki, A.; Burzykowski, T. Linear Mixed-Effects Models Using R; Springer Texts in Statistics; Springer: New York, NY, USA, 2013; ISBN 978-1-4614-3899-1. [Google Scholar]
- Lenth, R. Emmeans: Estimated marginal means, aka least-squares means. R Package, v. 1, n. 2, 2018. [Google Scholar]
- Pinheiro, J.; Bates, D.; Debroy, S.; Sakar, D. The R Core Team. nlme: Linear and Nonlinear Mixed Effects Models; R Core Team: Vienna, Austria, 2016. [Google Scholar]
- Thurau, C.; Kersting, K.; Bauckhage, C.; Augustin, F.I.S. Yes We Can—Simplex Volume Maximization for Descriptive Web-Scale Matrix Factorization. In Proceedings of the Conference on Information and Knowledge Management, Toronto, ON, Canada, 26–30 October 2010. [Google Scholar]
- Thurau, C.; Kersting, K.; Wahabzada, M.; Bauckhage, C. Descriptive matrix factorization for sustainability Adopting the principle of opposites. Data Min. Knowl. Discov.
**2012**, 24, 325–354. [Google Scholar] [CrossRef] - Kersting, K.; Bauckhage, C.; Wahabzada, M.; Mahlein, A.-K.; Steiner, U.; Oerke, E.-C.; Römer, C.; Plümer, L. Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants. In Computational Sustainability; Lässig, J., Kersting, K., Morik, K., Eds.; Springer International Publishing: Cham, Switzerland, 2016; Volume 645, pp. 99–120. ISBN 978-3-319-31856-1. [Google Scholar]
- Alfeld, M.; Wahabzada, M.; Bauckhage, C.; Kersting, K.; van der Snickt, G.; Noble, P.; Janssens, K.; Wellenreuther, G.; Falkenberg, G. Simplex Volume Maximization (SiVM): A matrix factorization algorithm with non-negative constrains and low computing demands for the interpretation of full spectral X-ray fluorescence imaging data. Microchem. J.
**2017**, 132, 179–184. [Google Scholar] [CrossRef] - Ghamisi, P.; Plaza, J.; Chen, Y.; Li, J.; Plaza, A.J. Advanced Spectral Classifiers for Hyperspectral Images: A review. IEEE Geosci. Remote Sens. Mag.
**2017**, 5, 8–32. [Google Scholar] [CrossRef] - Kruse, F.A.; Lefkoff, A.B.; Boardman, J.W.; Heidebrecht, K.B.; Shapiro, A.T.; Barloon, P.J.; Goetz, A.F.H. The spectral image processing system (SIPS)—Interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ.
**1993**, 44, 145–163. [Google Scholar] [CrossRef] - Barnes, R.J.; Dhanoa, M.S.; Lister, S.J. Standard Normal Variate Transformation and De-trending of Near-Infrared Diffuse Reflectance Spectra. Appl. Spectrosc.
**1989**, 43, 772–777. [Google Scholar] [CrossRef] - Mohd Asaari, M.S.; Mishra, P.; Mertens, S.; Dhondt, S.; Inzé, D.; Wuyts, N.; Scheunders, P. Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform. ISPRS J. Photogramm. Remote Sens.
**2018**, 138, 121–138. [Google Scholar] [CrossRef] - Naidu, R.A.; Perry, E.M.; Pierce, F.J.; Mekuria, T. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Comput. Electron. Agric.
**2009**, 66, 38–45. [Google Scholar] [CrossRef] - 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] [CrossRef] - Mahlein, A.-K.; Steiner, U.; Hillnhütter, C.; Dehne, H.-W.; Oerke, E.-C. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods
**2012**, 8, 3. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Kong, W.; Huang, W.; Zhou, X.; Ye, H.; Dong, Y.; Casa, R. Off-Nadir Hyperspectral Sensing for Estimation of Vertical Profile of Leaf Chlorophyll Content within Wheat Canopies. Sensors
**2017**, 17, 2711. [Google Scholar] [CrossRef] [PubMed] - Gitelson, A.A.; Keydan, G.P.; Merzlyak, M.N. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys. Res. Lett.
**2006**, 33, L11402. [Google Scholar] [CrossRef] - Gitelson, A.A.; Merzlyak, M.N.; Chivkunova, O.B. Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves. Photochem. Photobiol.
**2001**, 74, 38–45. [Google Scholar] [CrossRef] - Gitelson, A.A.; Gritz, Y.; Merzlyak, M.N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol.
**2003**, 160, 271–282. [Google Scholar] [CrossRef] - Vincini, M.; Frazzi, E.; D’Alessio, P. A broad-band leaf chlorophyll vegetation index at the canopy scale. Precis. Agric.
**2008**, 9, 303–319. [Google Scholar] [CrossRef] - Jordan, C.F. Derivation of Leaf-Area Index from Quality of Light on the Forest Floor. Ecology
**1969**, 50, 663–666. [Google Scholar] [CrossRef] - Le Maire, G.; François, C.; Dufrêne, E. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens. Environ.
**2004**, 89, 1–28. [Google Scholar] [CrossRef] - Zarco-Tejada, P.J.; Berjón, A.; López-Lozano, R.; Miller, J.R.; Martín, P.; Cachorro, V.; González, M.R.; de Frutos, A. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ.
**2005**, 99, 271–287. [Google Scholar] [CrossRef] - Gitelson, A.A.; Kaufman, Y.J.; Merzlyak, M.N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ.
**1996**, 58, 289–298. [Google Scholar] [CrossRef] - Gandia, S.; Fernández, G.; Garcia, J.C.; Moreno, J. Retrieval of vegetation biophysical variables from CHRIS/PROBA data in the SPARC campaign. In Proceedings of the 2nd CHRIS/Proba Workshop, Frascati, Italy, 28–30 April 2004; Volume 578, pp. 40–48. [Google Scholar]
- Lichtenthaler, H.K.; Lang, M.; Sowinska, M.; Heisel, F.; Miehé, J.A. Detection of Vegetation Stress Via a New High Resolution Fluorescence Imaging System. J. Plant Physiol.
**1996**, 148, 599–612. [Google Scholar] [CrossRef] - Daughtry, C.S.T.; Walthall, C.L.; Kim, M.S.; De Colstoun, E.B.; McMurtrey, J.E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ.
**2000**, 74, 229–239. [Google Scholar] [CrossRef] - Wu, C.; Niu, Z.; Tang, Q.; Huang, W. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agric. For. Meteorol.
**2008**, 148, 1230–1241. [Google Scholar] [CrossRef] - Haboudane, D. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ.
**2004**, 90, 337–352. [Google Scholar] [CrossRef] - Maccioni, A.; Agati, G.; Mazzinghi, P. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J. Photochem. Photobiol. B Biol.
**2001**, 61, 52–61. [Google Scholar] [CrossRef] - Chen, J.M. Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications. Can. J. Remote Sens.
**1996**, 22, 229–242. [Google Scholar] [CrossRef] - Dash, J.; Curran, P.J. The MERIS terrestrial chlorophyll index. Int. J. Remote Sens.
**2004**, 25, 5403–5413. [Google Scholar] [CrossRef] - Barnes, E.M.; Clarke, T.R.; Richards, S.E.; Colaizzi, P.D.; Haberland, J.; Kostrzewski, M.; Waller, P.; Choi, C.; Riley, E.; Thompson, T. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA, 16–19 July 2000; Volume 1619. [Google Scholar]
- Rouse, J.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS. In Proceedings of the NASA Goddard Space Flight Center Third ERTS-1 Symposium, Washington, DC, USA, 10–14 December 1973; NASA: Greenbelt, MD, USA, 1974. [Google Scholar]
- Gitelson, A.; Merzlyak, M.N. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. B Biol.
**1994**, 22, 247–252. [Google Scholar] [CrossRef] - Gong, P.; Pu, R.; Biging, G.S.; Larrieu, M.R. Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data. IEEE Trans. Geosci. Remote Sens.
**2003**, 41, 1355–1362. [Google Scholar] [CrossRef] - Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ.
**1996**, 55, 95–107. [Google Scholar] [CrossRef] - Gamon, J.A. The photochemical reflectance index: An optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia
**1997**, 112, 492–501. [Google Scholar] [CrossRef] [PubMed] - Blackburn, G.A. Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches. Remote Sens. Environ.
**1998**, 66, 273–285. [Google Scholar] [CrossRef] - Merzlyak, M.N.; Gitelson, A.A.; Chivkunova, O.B.; Rakitin, V.Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant.
**1999**, 106, 135–141. [Google Scholar] [CrossRef] - Chappelle, E.W.; Kim, M.S.; McMurtrey, J.E. Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sens. Environ.
**1992**, 39, 239–247. [Google Scholar] [CrossRef] - Roujean, J.-L.; Breon, F.-M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ.
**1995**, 51, 375–384. [Google Scholar] [CrossRef] - Guyot, G.; Baret, F. Utilisation de la haute resolution spectrale pour suivre l’etat des couverts vegetaux. In Proceedings of the 4th International Colloquium on Spectral Signatures of Objects in Remote Sensing, Aussois, France, 18–22 January 1988; ESA: Aussois, France, 1988; Volume 287, pp. 279–286. [Google Scholar]
- Peñuelas, J.; Baret, F.; Filella, I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica
**1995**, 31, 221–230. [Google Scholar] - Gitelson, A.A.; Merzlyak, M.N. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens.
**1997**, 18, 2691–2697. [Google Scholar] [CrossRef] - McMurtrey, J.E.; Chappelle, E.W.; Kim, M.S.; Meisinger, J.J.; Corp, L.A. Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sens. Environ.
**1994**, 47, 36–44. [Google Scholar] [CrossRef] - Zarco-Tejada, P.J.; Pushnik, J.C.; Dobrowski, S.; Ustin, S.L. Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sens. Environ.
**2003**, 84, 283–294. [Google Scholar] [CrossRef] - Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ.
**2002**, 81, 416–426. [Google Scholar] [CrossRef] - Haboudane, D.; Tremblay, N.; Miller, J.R.; Vigneault, P. Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived from Hyperspectral Data. IEEE Trans. Geosci. Remote Sens.
**2008**, 46, 423–437. [Google Scholar] [CrossRef] - Broge, N.H.; Leblanc, E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ.
**2001**, 76, 156–172. [Google Scholar] [CrossRef] - Clevers, J. The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sens. Environ.
**1988**, 25, 53–69. [Google Scholar] [CrossRef]

**Figure 1.**Distribution of experimental plots and treatments (T1, non-mixed system; T2, mixed system) in the study site. Figures correspond to false color composite (735, 631, and 609 nm as RGB bands) for UAV imagery acquired 37 (

**a**), 50 (

**b**), 64 (

**c**), and 78 (

**d**) days after planting (DAP). White boundaries indicate small and large experimental plots. Original experimental arrangement is indicated by black connectors and new blocks used for treatments comparison (as described in Section 2.6) are indicated in blue.

**Figure 2.**Specifications of the data acquired with the hyperspectral imaging system mounted on the UAV platform (red boxes, 16 spectral bands) and on handheld configuration (green boxes, 31 spectral bands; Section 2.3). Center line in each box indicate spectral band center and extremities for the full width at half maximum for each band (FWHM; varying between 13 and 21 nm for UAV data and between 13 and 23 nm for ground-based images).

**Figure 3.**Leaf chlorophyll content (

**a**), canopy height (

**b**), ground cover (

**c**), and vegetation indices (Table A2), namely, CIre (Chlorophyll Index red edge,

**d**), REP (Red Edge Position,

**e**), and WDVI (Weighted Difference Vegetation Index,

**f**) derived from UAV imagery, describing crop growth under different cultivation systems (T1 and T2, “non-mixed” and “mixed” treatments, respectively). Points indicate within plot measurements (n = 3 sampling units per plot), while each cross represent the average at plot level. Lines connect the average for each treatment over time. Numbers in blue correspond to the p-value for each acquisition date. Asterisks communicate the same p-values, indicating contrasts significant at 0.05 (*), 0.01 (**), and 0.001 (***).

**Figure 4.**Distribution of visual disease scores into specific classes of late blight severity (according to approximate percentage of affected leaf area at sampling unit level) for each assessment date. T1 and T2 correspond to systems cultivated with a single cultivar (“non-mixed”) and with a mixture of different cultivars (“mixed”), respectively. Only assessments made 64 and 78 Days After Planting (DAP) were followed by acquisition of ground-based and UAV data (*).

**Figure 5.**Average reflectance for pixels from T1 (“non-mixed” system) grouped according to log-likelihood ratio (LLR) in discrete intervals, between 0 to 15, in steps of 0.5. LLR, compares pixel-wise probability estimated for T1 (H1) in contrast to T2 (H0; “mixed” system). Ground (

**a**,

**c**,

**e**,

**g**) and UAV-based (

**b**,

**d**,

**f**,

**h**) data are presented for all acquisition dates. Colors of the average spectral signatures indicate the average LLR value for pixels included in a given interval. Ratio indicates the results for the division of the reflectance (band-wise) corresponding to a given LLR interval by that from the interval with the lowest LLR values (i.e., pixels with LLR below 0.5; indicated by blue dashed line). Delta (Δ) corresponds to the percentage of observations (pixels) within a given LLR interval for T1 subtracted from the percentage of observations in the same LLR interval for T2 (reference group). Delta is plotted in front of the average spectral signatures representing each LLR interval. Delta total (Δt) indicates absolute cumulated delta values.

**Figure 6.**Average reflectance for pixels within discrete intervals of log-likelihood ratio (LLR) between 0 and 5.5, in steps of 0.5. LLR, in this case, compares pixel-wise probability estimated for diseased sampling units (SUs; H1; ≤1.0%, ≤2.5%, ≤5.0%, ≤7.0%, ≤10.0% and ≤15.0% disease severity, in

**a**–

**f**, respectively) in contrast to healthier SUs (H0; only healthy plants for 64 DAP or disease severity below 1.0% for 78 DAP). Colors of the spectral curves indicate the average LLR value for pixels included in a given interval. Ratio indicates the division of reflectance corresponding to a given LLR interval by that from the interval with the lowest LLR values (i.e., for pixels with LLR below 0.5; indicated by the blue line). Delta (Δ) corresponds to the percentage of observations (pixels) within a given LLR interval for SUs from a specific disease severity class subtracted from the percentage of observations in the same LLR interval for the healthier SUs used as reference. Delta is plotted in front of each average spectral signature for the corresponding LLR interval. Delta total (Δt) indicates absolute cumulated delta values.

**Figure 7.**Distribution of log-likelihood ratio (LLR) within sampling units (SUs) scored for late blight development 64 and 78 DAP. Date of UAV image acquisition and corresponding disease severity class (DS) are indicated above the images representing eight SUs selected from those observed for each class. Crop patches cultivated with T1 (“non-mixed” system) are indicated by red frames and those cultivated with T2 (“mixed” system) by black frames (images chosen for illustration were randomly selected from those observed in each disease severity class, as indicated in Table A5). Diseased severity classes from up to 1.0% until between 10.0% and 15.0% are represented in images (

**a**–

**f**). Scale bars in the left upper corner of each image represent 25 cm.

**Figure 8.**Distribution of vegetation indices (VIs; CIre (

**a**–

**c**); REP (

**d**–

**f**); WDVI (

**g**–

**i**)) values for sampling units within different disease severity (DS) classes. Only selected VIs providing relatively good discriminative potential between healthier references, and the DS classes considered (Table A8) for UAV imagery acquire 78 DAP are presented. Green dots indicate pixels within a given DS class (from ≤1.0% up to between 10.0% and 15.0%), while red dots and red error bars correspond to median and standard deviation for these observations. Values in parentheses indicate the log-likelihood ratio (LLR) threshold used to selected pixels in a given percentile. Black dashed lines separate healthier observations (references—*) from other DS classes. Blue dashed lines indicate the average VI value for pixels included in the references. It is worth noting that for the percentiles, two distinct sets of pixels represent the reference, one for each DS class above 1.0% DS.

**Figure 9.**Distribution of visual scores into specific classes of late blight severity (according to the approximate percentage of affected leaf area) in SUs evaluated 78 DAP. Experimental plots 1–8 are indicated by figures (

**a**–

**h**). Background images include values of OSAVI (Optimized Soil Adjusted Vegetation Index; VI) for pixels retained after vegetation segmentation and a false color composite (833, 663, and 609 nm as RGB bands).

**Figure 10.**Log-likelihood ratio (LLR) for UAV data acquired 78 DAP (i.e., last data acquisition). LLR represents the comparison of pixel-wise probability considering distributions for diseased SUs (H1: ≤ 7.0% severity) and a healthy reference (H0: up to 1.0% late blight severity). OSAVI values are indicated in grey scale (VI). Experimental plots 1–8 are represented in figures (

**a**–

**h**).

**Figure 11.**Distribution of log-likelihood ratio (LLR) values derived for patches of UAV images acquired 64 and 78 DAP. Black lines correspond to LLR extracted from sampling units (SUs) within the late blight severity level (disease severity (DS)) considered as hypothesis H1 (≤1.0%, ≤2.5%, ≤5.0%, ≤7.0%, ≤10.0% and ≤15.0% of disease severity in

**a**–

**f**, respectively), while comparing with healthier plants (H0, completely healthy for 64 DAP and ≤1.0% severity for 78 DAP). Green lines indicate the distribution of LLR values for SUs with lower severity levels than the class considered in each case (e.g., all sampling units with disease severity ≤1.0% in

**b**). Red lines illustrate the distribution of LLR values for SUs with higher severity levels than the class considered in each case (e.g., all sampling units with >2.5% of disease severity in

**b**).

**Table 1.**General aspects related to the data acquisition using the Unmanned Aerial Vehicle (UAV) platform and a ground-based sensing setup (Section 2.3). Days after planting (DAP), estimate general crop growth stage according to the BBCH (‘Biologische Bundesanstalt, Bundessortenamt and CHemical industry’) scale [50], illumination conditions, and number (nbr.) of ground control points (GCPs) used during photogrammetric processing.

Date ^{1} | DAP | Growth Stage ^{2} | Ground Data ^{3} | Illum. ^{4} | Integration Time (ms) | Nbr. of GCPs |
---|---|---|---|---|---|---|

26/05 | 37 | 2–4 | I, II | Sunny | 10 | 4 |

08/06 | 50 | 4–6 | I, II | Sunny | 10 | 4 |

22/06 | 64 | 6–7 | I, II, III | Cloudy | 20 | 8 |

06/07 | 78 | 7–8 | I, II, III | Sunny | 10 | 7 |

^{1}Flights were realized between 10:00 h and 13:00 h (GTM+2) to minimize angular effects of incident radiance on the measurements.

^{2}BBCH scale summary: 0 = germination, 1 = leaf development, 2 = formation of basal side shoots, 3 = main stem elongation, 4 = tuber formation, 5 = inflorescence emerging, 6 = flowering, 7 = fruit development, 8 = ripening of fruit and seed, 9 = senescence.

^{3}I = crop traits (leaf chlorophyll content, canopy height), II = canopy spectra acquired with camera in handheld mode for a selection of SUs, III = late blight severity assessment.

^{4}Sunny illumination conditions corresponds to clear sky while cloudy indicates partially overcast conditions.

**Table 2.**Kendall-tau correlation coefficients between disease severity classes (as ordinal variable) and median of log-likelihood ratio (LLR as continuous variable) at sampling unit level for assessment made 64 and 78 DAP. Values are given for each disease severity class used to derive probability distributions, which were compared with the distribution for the reference class (only healthy patches for 64 DAP and ≤1.0% severity for 78 DAP) during estimation of pixel-wise LLR.

Dataset | Disease Severity Class Considered for LLR Calculation | |||||
---|---|---|---|---|---|---|

64 DAP ^{2} | 78 DAP ^{2} | |||||

≤1.0 ^{1} | ≤2.5 ^{1} | ≤5.0 ^{1} | ≤7.0 ^{1} | ≤10.0 ^{1} | ≤15.0 ^{1} | |

All pixels | 0.249 * | 0.020 | 0.321 *** | 0.592 *** | 0.519 *** | 0.522 *** |

Upper 20th percentile of LLR | 0.286 * | −0.029 | 0.106 | 0.562 *** | 0.534 *** | 0.524 *** |

Upper 10th percentile of LLR | 0.313 * | −0.038 | 0.074 | 0.556 *** | 0.537 *** | 0.516 *** |

^{1}Significant at 0.05 (*), 0.01 (**) or 0.001 (***) level;

^{2}only observations from T1 considered for 64 DAP while data corresponding to both treatments were used for 78 DAP.

© 2019 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Franceschini, M.H.D.; Bartholomeus, H.; van Apeldoorn, D.F.; Suomalainen, J.; Kooistra, L.
Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato. *Remote Sens.* **2019**, *11*, 224.
https://doi.org/10.3390/rs11030224

**AMA Style**

Franceschini MHD, Bartholomeus H, van Apeldoorn DF, Suomalainen J, Kooistra L.
Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato. *Remote Sensing*. 2019; 11(3):224.
https://doi.org/10.3390/rs11030224

**Chicago/Turabian Style**

Franceschini, Marston Héracles Domingues, Harm Bartholomeus, Dirk Frederik van Apeldoorn, Juha Suomalainen, and Lammert Kooistra.
2019. "Feasibility of Unmanned Aerial Vehicle Optical Imagery for Early Detection and Severity Assessment of Late Blight in Potato" *Remote Sensing* 11, no. 3: 224.
https://doi.org/10.3390/rs11030224