Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria
Abstract
:1. Introduction
1.1. Root Symptoms
1.2. Foliar Symptoms
1.3. Disease Diagnosis
1.4. The Potential of Hyperspectral Sensors
2. Materials and Methods
2.1. Study Site
2.2. Foliar Sampling
2.3. Root Sampling and Inspection
2.4. Plant Groups
2.5. Hyperspectral Data Acquisition
2.6. Statistical Analyses
2.7. Accuracy Assessment
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
No. | Index | p-Value | Diseased vs. Asymptomatic | Healthy vs. Asymptomatic | Healthy vs. Diseased |
---|---|---|---|---|---|
Cross-Validation P-adj | |||||
1 | GDVI | 0.000004 | 0.00005 | 0.52294 | 0.00002 |
2 | MGVI | 0.00004 | 0.00130 | 0.27417 | 0.00009 |
3 | OSAVI | 0.00009 | 0.00016 | 1 | 0.00259 |
4 | NDchl | 0.00011 | 0.00009 | 0.05128 | 0.35577 |
5 | mARI | 0.00012 | 0.00004 | 0.87102 | 0.05249 |
6 | Ctr4 | 0.00016 | 0.00019 | 0.02559 | 0.60489 |
7 | Chlred-edge | 0.00016 | 0.00018 | 0.03003 | 0.58745 |
8 | REIP3 | 0.00019 | 0.00045 | 0.00916 | 0.95927 |
9 | SR750/710 | 0.00020 | 0.00026 | 0.02886 | 0.58745 |
10 | GNDVI | 0.00021 | 0.00014 | 0.19992 | 0.15410 |
11 | NDVI | 0.00021 | 0.00008 | 0.49839 | 0.13671 |
12 | LCI | 0.00023 | 0.00023 | 0.05320 | 0.43261 |
13 | AVI | 0.00025 | 0.03044 | 0.09668 | 0.00014 |
14 | DVI | 0.00039 | 0.00014 | 0.88900 | 0.11608 |
15 | Vog2 | 0.00122 | 0.00155 | 0.04942 | 1.00000 |
16 | mCRIRE | 0.00219 | 0.05001 | 0.25925 | 0.00276 |
17 | ARI | 0.00908 | 0.00451 | 0.98258 | 0.62269 |
18 | WBI | 0.15533 | 0.22688 | 0.70442 | 1 |
No. | Index | p-Value | F-Value | Diseased vs. Asymptomatic | Healthy vs. Asymptomatic | Healthy vs. Diseased |
---|---|---|---|---|---|---|
Cross-Validation P-adj | ||||||
1 | GDVI | 6.6 × 10−7 | 16.42 | 1.4 × 10−5 | 5.0 × 10−1 | 8.7 × 10−6 |
2 | NDchl | 1.2 × 10−5 | 12.69 | 6.7 × 10−6 | 6.3 × 10−2 | 1.6 × 10−1 |
3 | MGVI | 1.3 × 10−5 | 12.63 | 4.3 × 10−4 | 3.0 × 10−1 | 3.9 × 10−5 |
4 | OSAVI | 1.4 × 10−5 | 12.50 | 9.6 × 10−5 | 7.4 × 10−1 | 1.7 × 10−4 |
5 | GNDVI | 1.5 × 10−5 | 12.42 | 7.6 × 10−6 | 1.7 × 10−1 | 6.6 × 10−2 |
6 | NDVI | 1.7 × 10−5 | 12.24 | 9.00 × 10−6 | 2.50 × 10−1 | 4.26 × 10−2 |
7 | LCI | 1.8 × 10−5 | 12.17 | 1.0 × 10−5 | 7.2 × 10−2 | 1.7 × 10−1 |
8 | Ctr4 | 2.0 × 10−5 | 12.07 | 1.3 × 10−5 | 3.8 × 10−2 | 2.8 × 10−1 |
9 | REIP3 | 2.2 × 10−5 | 11.94 | 1.5 × 10−5 | 3.5 × 10−2 | 3.1 × 10−1 |
10 | Chlred.edge | 2.6 × 10−5 | 11.71 | 2.0 × 10−5 | 3.1 × 10−2 | 3.6 × 10−1 |
11 | mARI | 2.8 × 10−5 | 11.62 | 1.7 × 10−5 | 4.3 × 10−1 | 2.4 × 10−2 |
12 | SR750.710 | 3.7 × 10−5 | 11.30 | 2.8 × 10−5 | 3.2 × 10−2 | 4.0 × 10−1 |
13 | AVI | 7.3 × 10−5 | 10.47 | 7.6 × 10−3 | 1.0 × 10−1 | 7.2 × 10−5 |
14 | DVI | 9.1 × 10−5 | 10.21 | 5.4 × 10−5 | 4.4 × 10−1 | 4.3 × 10−2 |
15 | Vog2 | 2.7 × 10−4 | 8.90 | 2.6 × 10−4 | 3.9 × 10−2 | 6.3 × 10−1 |
16 | mCRIRE | 2.5 × 10−3 | 6.35 | 6.5 × 10−2 | 1.9 × 10−1 | 2.1 × 10−3 |
17 | ARI | 5.5 × 10−3 | 5.47 | 3.7 × 10−3 | 4.7 × 10−1 | 2.8 × 10−1 |
18 | WBI | 2.8 × 10−1 | 1.28 | 3.0 × 10−1 | 5.0 × 10−1 | 9.9 × 10−1 |
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Number | Vegetation Index | Abbreviation | Equation | Related to | Reference |
---|---|---|---|---|---|
1 | Anthocyanin Reflectance Index | ARI | anthocyanins | [43] | |
2 | Modified Anthocyanin Reflectance Index | mARI | anthocyanins | [44] | |
3 | Carotenoid Reflectance Index Red Edge | mCRIRE | carotenoid | [44] | |
4 | Normalized Difference Chlorophyll | NDchl | chlorophyll | [45] | |
5 | Red Edge Inflection Point 3 | REIP3 | chlorophyll | [46] | |
6 | Leaf Chlorophyll Index | LCI | chlorophyll | [47] | |
7 | Vogelmann Indices 2 | Vog2 | chlorophyll | [24] | |
8 | Zarco-Tejada and Miller | SR750/710 | chlorophyll | [48] | |
9 | Chlorophyll Red Edge | Chlred-edge | chlorophyll | [44] | |
10 | Difference Vegetation Index | DVI | vegetation | [49] | |
11 | Normalized Difference Vegetation Index | NDVI | vegetation | [49] | |
12 | Misra Green Vegetation Index | MGVI | vegetation | [49] | |
13 | Green Normalized Difference Vegetation Index | GNDVI | vegetation | [50] | |
14 | Ashburn Vegetation Index | AVI | vegetation | [49] | |
15 | Green Difference Vegetation Index | GDVI | vegetation | [51] | |
16 | Optimized Soil-Adjusted Vegetation Index | OSAVI | vegetation | [48] | |
17 | Simple Ratio Carter4 | Ctr4 | stress | [52] | |
18 | Water Band Index | WBI | water content | [42] |
Model | Groups | Variables Used |
---|---|---|
1 | Healthy vs. Diseased | GDVI, MGVI, AVI, OSAVI, R920 |
2 | Healthy vs. Asymptomatic | R705, R711, R708, R714, R717 |
3 | Healthy vs. Asymptomatic vs. Diseased | GDVI, NDchl, MGVI, OSAVI, GNDVI |
Overall Accuracy | Kappa Coefficient | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Model | SkNN | LDA | QDA | RDA | NB | RPART | Mean | Std.Dev. | Model | SkNN | LDA | QDA | RDA | NB | RPART | Mean | Std.Dev. |
1 | 80% | 90% | 80% | 90% | 90% | 95% | 88% | 0.061 | 1 | 0.6 | 0.78 | 0.57 | 0.78 | 0.76 | 0.89 | 0.73 | 0.122 |
2 | 72% | 72% | 80% | 76% | 76% | 72% | 75% | 0.033 | 2 | 0.29 | 0.23 | 0.49 | 0.31 | 0.31 | 0.34 | 0.33 | 0.087 |
3 | 60% | 57% | 54% | 54% | 76% | 68% | 62% | 0.088 | 3 | 0.32 | 0.3 | 0.27 | 0.27 | 0.6 | 0.42 | 0.36 | 0.128 |
Mean | 71% | 73% | 71% | 73% | 81% | 78% | Mean | 0.40 | 0.44 | 0.44 | 0.45 | 0.56 | 0.55 | ||||
Std.Dev. | 0.101 | 0.165 | 0.150 | 0.181 | 0.081 | 0.146 | Std.Dev. | 0.171 | 0.299 | 0.155 | 0.284 | 0.228 | 0.297 | ||||
Error of Omission | Error of Commission | ||||||||||||||||
Model | SkNN | LDA | QDA | RDA | NB | RPART | Mean | Std.Dev. | Model | SkNN | LDA | QDA | RDA | NB | RPART | Mean | Std.Dev. |
1 d | 29% | 14% | 21% | 14% | 7% | 7% | 15% | 0.085 | 1 d | 0% | 0% | 17% | 0% | 6% | 0% | 4% | 0.069 |
2 a | 12% | 6% | 6% | 0% | 0% | 17% | 7% | 0.067 | 2 a | 63% | 75% | 50% | 75% | 75% | 50% | 65% | 0.123 |
3 a | 32% | 42% | 42% | 47% | 21% | 5% | 32% | 0.160 | 3 a | 50% | 39% | 33% | 39% | 22% | 50% | 39% | 0.106 |
3 d | 30% | 20% | 30% | 20% | 20% | 30% | 25% | 0.055 | 3 d | 18% | 26% | 26% | 30% | 15% | 11% | 21% | 0.074 |
3 h | 75% | 75% | 75% | 75% | 37% | 100% | 73% | 0.202 | 3 h | 3% | 7% | 14% | 7% | 37% | 0% | 11% | 0.134 |
Mean | 36% | 31% | 35% | 31% | 17% | 32% | Mean | 27% | 29% | 28% | 30% | 31% | 22% | ||||
Std.Dev. | 0.234 | 0.278 | 0.260 | 0.298 | 0.143 | 0.394 | Std.Dev. | 0.283 | 0.298 | 0.144 | 0.297 | 0.271 | 0.258 |
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Calamita, F.; Imran, H.A.; Vescovo, L.; Mekhalfi, M.L.; La Porta, N. Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria. Remote Sens. 2021, 13, 2436. https://doi.org/10.3390/rs13132436
Calamita F, Imran HA, Vescovo L, Mekhalfi ML, La Porta N. Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria. Remote Sensing. 2021; 13(13):2436. https://doi.org/10.3390/rs13132436
Chicago/Turabian StyleCalamita, Federico, Hafiz Ali Imran, Loris Vescovo, Mohamed Lamine Mekhalfi, and Nicola La Porta. 2021. "Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria" Remote Sensing 13, no. 13: 2436. https://doi.org/10.3390/rs13132436
APA StyleCalamita, F., Imran, H. A., Vescovo, L., Mekhalfi, M. L., & La Porta, N. (2021). Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria. Remote Sensing, 13(13), 2436. https://doi.org/10.3390/rs13132436