Identification of Early Heat and Water Stress in Strawberry Plants Using Chlorophyll-Fluorescence Indices Extracted via Hyperspectral Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Imaging
2.2. Chlorophyll-Fluorescence Indices
2.2.1. Pigment-Specific Normalized Difference for Chlorophyll A (PSNDa)
2.2.2. Pigment-Specific Normalized Difference for Chlorophyll B (PSNDb)
2.2.3. Pigment-Specific Simple Ratio for Chlorophyll A (PSSRa)
2.2.4. Pigment-Specific Simple Ratio for Chlorophyll B (PSSRb)
2.2.5. Chlorophyll Index at Red Edge (CI-RedEdge)
2.2.6. Normalized-Difference Red-Edge Index (NDRE)
2.2.7. Simple Ratio (SR)
2.2.8. Red-Edge Vegetation-Stress Index (RVSI)
2.3. System Configuration and Packages Used
2.4. Machine-Learning Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Type | Count | Resolution |
---|---|---|
Controlled | 45 | 502 × 500 × 128 |
stressed | 43 | 502 × 500 × 128 |
Recovered | 43 | 502 × 500 × 128 |
Models and Scores | Class | SVM | Bagging | Boosting | ||||
---|---|---|---|---|---|---|---|---|
RBF | Polynomial | Decision Tree | Random Forest | Adaboost | Gradient Boosting | XG Boosting | ||
Precision | Controlled | 0.67 | 1.00 | 1.00 | 1.00 | 1.00 | 0.86 | 0.88 |
Stressed | 0.56 | 0.50 | 0.44 | 0.80 | 0.35 | 0.58 | 0.64 | |
Recovered | 0.73 | 0.71 | 0.62 | 1.00 | 0.00 | 0.86 | 0.86 | |
Recall | Controlled | 0.46 | 0.15 | 0.85 | 0.92 | 0.92 | 0.46 | 0.54 |
Stressed | 0.62 | 0.88 | 0.50 | 1.00 | 0.88 | 0.88 | 0.88 | |
Recovered | 0.92 | 1.00 | 0.67 | 0.92 | 0.00 | 1.00 | 1.00 | |
F1 Score | Controlled | 0.55 | 0.27 | 0.92 | 0.96 | 0.96 | 0.60 | 0.67 |
Stressed | 0.59 | 0.64 | 0.47 | 0.89 | 0.50 | 0.70 | 0.74 | |
Recovered | 0.81 | 0.83 | 0.64 | 0.96 | 0.00 | 0.92 | 0.92 | |
Overall Accuracy | 0.67 | 0.64 | 0.70 | 0.94 | 0.57 | 0.76 | 0.79 |
Models and Scores | Class | SVM | Bagging | Boosting | ||||
---|---|---|---|---|---|---|---|---|
RBF | Polynomial | Decision Tree | Random Forest | Adaboost | Gradient Boosting | XG Boosting | ||
Precision | Controlled | 0.67 | 1.00 | 1.00 | 0.91 | 1.00 | 0.92 | 0.86 |
Stressed | 0.56 | 0.50 | 0.73 | 0.78 | 0.35 | 0.78 | 0.58 | |
Recovered | 0.73 | 0.71 | 0.79 | 0.85 | 0.00 | 1.00 | 0.86 | |
Recall | Controlled | 0.46 | 0.15 | 0.62 | 0.77 | 0.92 | 0.85 | 0.46 |
Stressed | 0.62 | 0.88 | 1.00 | 0.88 | 0.88 | 0.88 | 0.88 | |
Recovered | 0.92 | 1.00 | 0.92 | 0.92 | 0.00 | 1.00 | 1.00 | |
F1 Score | Controlled | 0.55 | 0.27 | 0.76 | 0.83 | 0.96 | 0.88 | 0.60 |
Stressed | 0.59 | 0.64 | 0.84 | 0.82 | 0.50 | 0.82 | 0.70 | |
Recovered | 0.81 | 0.83 | 0.85 | 0.88 | 0.00 | 1.00 | 0.92 | |
Overall Accuracy | 0.67 | 0.64 | 0.82 | 0.85 | 0.57 | 0.91 | 0.76 |
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Poobalasubramanian, M.; Park, E.-S.; Faqeerzada, M.A.; Kim, T.; Kim, M.S.; Baek, I.; Cho, B.-K. Identification of Early Heat and Water Stress in Strawberry Plants Using Chlorophyll-Fluorescence Indices Extracted via Hyperspectral Images. Sensors 2022, 22, 8706. https://doi.org/10.3390/s22228706
Poobalasubramanian M, Park E-S, Faqeerzada MA, Kim T, Kim MS, Baek I, Cho B-K. Identification of Early Heat and Water Stress in Strawberry Plants Using Chlorophyll-Fluorescence Indices Extracted via Hyperspectral Images. Sensors. 2022; 22(22):8706. https://doi.org/10.3390/s22228706
Chicago/Turabian StylePoobalasubramanian, Mangalraj, Eun-Sung Park, Mohammad Akbar Faqeerzada, Taehyun Kim, Moon Sung Kim, Insuck Baek, and Byoung-Kwan Cho. 2022. "Identification of Early Heat and Water Stress in Strawberry Plants Using Chlorophyll-Fluorescence Indices Extracted via Hyperspectral Images" Sensors 22, no. 22: 8706. https://doi.org/10.3390/s22228706