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
"> Figure 1
<p>Tomato leaves collected to measure their spectral reflectance—tomato yellow leaf curl (TYLC)-infected leaves: (<b>a</b>) tolerant variety, (<b>b</b>) susceptible variety, and leaves infected by (<b>c</b>) target spot and (<b>d</b>) bacterial spot.</p> "> Figure 2
<p>(<b>a</b>) Benchtop Pika L 2.4 camera with leaf samples in the laboratory condition. (<b>b</b>) Unmanned aerial vehicle (UAV) using hyperspectral sensing to collect spectral reflectance data from susceptible and tolerant tomato plants infected with tomato yellow leaf curl. The data were collected after 50–60 days from transplanting on 30 April 2019.</p> "> Figure 3
<p>The spectral reflectance signatures of healthy, TYLC-infected (tolerant and susceptible varieties) tomato plants: (<b>a</b>) in laboratory conditions and (<b>b</b>) field conditions.</p> "> Figure 4
<p>The spectral reflectance signatures of TYLC (on susceptible and tolerant tomato varieties), bacterial spot (BS), and target spot (TS) infected tomato plants: (<b>a</b>) in laboratory conditions and (<b>b</b>) field conditions (UAV-based).</p> "> Figure 5
<p>The M statistic value of vegetation indices for TYLC, BS, and TS diseases in the laboratory: (<b>a</b>) first group of VIs and (<b>b</b>) second group of VIs. The vertical bar represents the error bar.</p> "> Figure 6
<p>The M statistic value of vegetation indices (VIs) for TYLC, BS, and TS diseases in the field (UAV-based): (<b>a</b>) first group of VIs, and (<b>b</b>) second group of VIs. The vertical lines present error bars.</p> ">
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