Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Tomato Yellow Leaf Curl (TYLC) Sample Collection
2.2. Target Spot and BS Sample Collection
2.2.1. Inoculation Methods
Tomato Yellow Leaf Curl Disease
Target Spot
Bacterial Spot
2.3. Laboratory Data Collection
2.4. UAV-Based Data Collection
2.5. Classification Methods
2.6. Vegetation Indices
Data Analysis for Selecting VIs
3. Results and Discussion
3.1. Spectral Reflectance Analysis
3.1.1. Spectral Reflectance of TYLC, BS, and TS Diseases: Laboratory-Based Analysis
3.1.2. Spectral Reflectance of TYLC, BS, and TS Diseases: Field (UAV)-Based Analysis
3.2. Classification Results
3.3. Significant VIs for Disease Detection: Laboratory-Based Analysis
3.4. Significant VIs for Disease Detection: Field (UAV)-Based Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Vegetation Indices | Equations | References |
---|---|---|
Ratio Analysis of reflectance Spectral Chlorophyll-a (RARSa) | [29] | |
Ratio Analysis of reflectance Spectral Chlorophyll b (RARSb) | [29] | |
Ratio analysis of reflectance spectra (RARSc) | [29] | |
Pigment specific simple ratio (PSSRa) | [30] | |
Normalized difference vegetation index 780 (NDVI 780) | [31] | |
Structure Insensitive Pigment Index (SIPI) | [32] | |
Normalized phaeophytinization index (NPQI) | [33] | |
Red-Edge Vegetation Stress Index 1 (RVS1) | [34] | |
Triangle Vegetation Index (TVI) | [35] | |
Renormalized Difference Vegetation Index (RDVI) | [36] | |
Normalized difference vegetation index 850 (NDVI850) | [31] | |
Simple Ratio Index (SR 761) | [37] | |
Simple Ratio Index (SR 850) | This study | |
Simple Ratio Index (SR 900) | This study | |
Water Stress and Canopy Temperature (NWI 2) | [38] | |
Green NDVI (GNDVI) | [39] | |
Photochemical Reflectance Index (PRI) | [40] | |
Modified Chlorophyll Absorption in Reflectance Index (mCARI 1) | [41] | |
Modified Triangular Vegetation Index1 (MTVI 1) | [41] | |
Modified Triangular Vegetation Index2 (MTVI 2) | [41] |
Parameter | STDA | RBF (%) | |||
---|---|---|---|---|---|
Overall Percent (%) | Cross Validation (%) | Wilks Lambda | Chi-Square | ||
Laboratory based | |||||
H vs. TYLC Tolerant-Asy | 100 | 100 | 0.014 | 388.0 | 89 |
H vs. TYLC Tolerant-Sym | 100 | 100 | 0.028 | 374.1 | 100 |
H vs. TYLC Susceptible-Asy | 100 | 100 | 0.023 | 320.2 | 83 |
H vs. TYLC Susceptible -Sym | 100 | 100 | 0.044 | 321.4 | 100 |
Tolerant-Asy vs. Susceptible-Asy | 100 | 100 | 0.086 | 91.8 | 100 |
Tolerant-Sym vs. Susceptible-Sym | 100 | 100 | 0.096 | 111.1 | 66.7 |
H vs. TS-Asy | 95 | 95 | 0.045 | 523.6 | 82 |
H vs. TS-Sym | 95 | 95 | 0.005 | 422.4 | 90 |
H vs. BS-Asy | 94 | 94 | 0.005 | 924.1 | 95 |
H vs. BS-Sym | 95 | 94 | 0.005 | 523.6 | 89 |
TS-Asy vs. BS-Asy | 88 | 87 | 0.306 | 145.0 | 83 |
Ts-Sym vs. BS-Sym | 82 | 82 | 0.456 | 120.3 | 46 |
Field (UAV) based | |||||
H vs. TYLC Tolerant | 100 | 100 | 0.006 | 539.9 | 100 |
H vs. TYLC Susceptible | 100 | 100 | 0.018 | 378.1 | 97 |
TYLC Tolerant vs. Susceptible | 100 | 100 | 0.104 | 146.2 | 76 |
H vs. TS | 98 | 96 | 0.026 | 597.8 | 98 |
H vs. BS | 96 | 96 | 0.013 | 541.5 | 93 |
Ts vs. BS | 82 | 80 | 0.457 | 141.9 | 64 |
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Abdulridha, J.; Ampatzidis, Y.; Qureshi, J.; Roberts, P. Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning. Remote Sens. 2020, 12, 2732. https://doi.org/10.3390/rs12172732
Abdulridha J, Ampatzidis Y, Qureshi J, Roberts P. Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning. Remote Sensing. 2020; 12(17):2732. https://doi.org/10.3390/rs12172732
Chicago/Turabian StyleAbdulridha, Jaafar, Yiannis Ampatzidis, Jawwad Qureshi, and Pamela Roberts. 2020. "Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning" Remote Sensing 12, no. 17: 2732. https://doi.org/10.3390/rs12172732
APA StyleAbdulridha, J., Ampatzidis, Y., Qureshi, J., & Roberts, P. (2020). Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning. Remote Sensing, 12(17), 2732. https://doi.org/10.3390/rs12172732