Chlorophyll Fluorescence Imaging Combined with Active Oxygen Metabolism for Classification of Similar Diseases in Cucumber Plants
Abstract
:1. Introduction
2. Materials and Methods
2.1. Plant Materials and Growth Conditions
2.2. Pathogen Preparation and Plant Inoculation
2.3. Chlorophyll Fluorescence Imaging System and Data Acquisition
2.4. Determination of Nitrogen, Chlorophyll, MDA and H2O2 Content and Enzyme Activity in Cucumber Leaves
2.5. Data Processing and Analyzing
3. Results and Discussion
3.1. Effects of Fungal Infection on Chlorophyll, Nitrogen, H2O2 and MDA Content in Cucumber Leaves
3.2. Effects of Fungal Infection on Activity of Antioxidative Enzymes in Cucumber Leaves
3.3. Effects of Fungal Infection in Chlorophyll Fluorescence Imagines of Diseased Cucumber Plants
3.4. Discriminant Results for Different Diseases in Cucumber Plants based on Multiple Chlorophyll Fluorescence Parameters
3.5. Discriminant Results for Early Diseased Cucumbers Based on Multiple Chlorophyll Fluorescence Parameters
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Infection Time | Brown Spot Group | Anthracnose Group | Control Group | |||
---|---|---|---|---|---|---|
Nitrogen | Chlorophyll | Nitrogen | Chlorophyll | Nitrogen | Chlorophyll | |
0 h | 3.57 ± 0.06 a | 50.26 ± 2.86 a | 3.57 ± 0.15 a | 49.92 ± 2.97 a | 3.58 ± 0.13 ab | 49.72 ± 2.76 a |
24 h | 3.42 ± 0.23 ab | 45.24 ± 5.05 b | 3.50 ± 0.18 a | 48.38 ± 3.35 a | 3.56 ± 0.13 ab | 48.81 ± 3.72 a |
48 h | 3.31 ± 0.22 bc | 44.07 ± 4.36 bc | 3.48 ± 0.16 ab | 48.18 ± 4.06 a | 3.64 ± 0.16 a | 48.29 ± 2.92 a |
72 h | 3.25 ± 0.24 cd | 43.31 ± 4.95 bc | 3.40 ± 0.28 bc | 46.12 ± 2.98 b | 3.49 ± 0.14 b | 47.71 ± 3.73 a |
Groups | Parameters | Infection Time | |||
---|---|---|---|---|---|
Healthy | 24 h | 48 h | 72 h | ||
Brown spot | Fv/Fm | 0.840 ± 0.020 a | 0.828 ± 0.017 a | 0.825 ± 0.019 a | 0.819 ± 0.020 a |
Y(II) | 0.214 ± 0.013 a | 0.200 ± 0.016 a | 0.211 ± 0.022 a | 0.217 ± 0.023 a | |
qP | 0.366 ± 0.040 ab | 0.339 ± 0.030 a | 0.394 ± 0.050 b | 0.446 ± 0.039 c | |
qN | 0.713 ± 0.047 a | 0.795 ± 0.015 b | 0.745 ± 0.017 a | 0.712 ± 0.020 a | |
Y(NPQ) | 1.386 ± 0.140 a | 1.513 ± 0.060 a | 1.871 ± 0.091 b | 1.873 ± 0.163 b | |
Anthracnose | Fv/Fm | 0.840 ± 0.020 a | 0.834 ± 0.024 a | 0.829 ± 0.025 a | 0.822 ± 0.026 a |
Y(II) | 0.214 ± 0.013 a | 0.195 ± 0.017 b | 0.196 ± 0.016 ab | 0.207 ± 0.014 ab | |
qP | 0.366 ± 0.040 ab | 0.345 ± 0.035 a | 0.396 ± 0.047 b | 0.438 ± 0.032 c | |
qN | 0.713 ± 0.047 ab | 0.783 ± 0.023 c | 0.755 ± 0.037 bc | 0.708 ± 0.021 a | |
Y(NPQ) | 1.386 ± 0.140 a | 1.506 ± 0.049 a | 1.772 ± 0.101 b | 1.801 ± 0.158 b |
Model | Class | Calibration (%) | Prediction | Class | Calibration (%) | Prediction | ||||
---|---|---|---|---|---|---|---|---|---|---|
Healthy | AN | Accuracy (%) | Healthy | AN | Accuracy (%) | |||||
(A) SVM | Healthy | 98.5 | 31 | 3 | 91.2 | Healthy | 100.0 | 31 | 3 | 91.2 |
BS | 95.0 | 3 | 37 | 92.5 | AN | 96.3 | 5 | 35 | 87.5 | |
Overall | 96.6 | 34 | 40 | 91.9 | Overall | 97.9 | 36 | 38 | 89.2 | |
(B) XG-Boost | Healthy | 100.0 | 31 | 3 | 91.2 | Healthy | 100.0 | 30 | 4 | 88.2 |
BS | 98.7 | 1 | 39 | 97.5 | AN | 96.3 | 4 | 36 | 90.0 | |
Overall | 99.3 | 32 | 42 | 94.6 | Overall | 97.9 | 34 | 40 | 89.2 |
Model | Class | Calibration (%) | Prediction | |||
---|---|---|---|---|---|---|
Healthy | AN | BS | Accuracy (%) | |||
(A) SVM | Healthy | 90.1 | 26 | 4 | 4 | 76.4 |
AN | 95.0 | 1 | 38 | 1 | 95.0 | |
BS | 92.5 | 5 | 2 | 33 | 82.5 | |
Overall | 92.9 | 32 | 44 | 38 | 85.1 | |
(B) XGBoost | Healthy | 89.4 | 28 | 3 | 3 | 82.4 |
AN | 98.8 | 1 | 39 | 0 | 97.5 | |
BS | 90.0 | 6 | 0 | 34 | 85.0 | |
Overall | 92.9 | 35 | 42 | 37 | 88.6 |
Model | Class | Calibration (%) | Prediction (%) | Class | Calibration (%) | Prediction (%) |
---|---|---|---|---|---|---|
(A) SVM | Healthy | 98.5 | 79.4 | Healthy | 97.5 | 85.3 |
BS-slight | 85.0 | 85.0 | AN-slight | 90 | 75.0 | |
BS-severe | 92.5 | 85.0 | AN-severe | 92.5 | 85.0 | |
Overall | 93.2 | 82.4 | Overall | 93.3 | 82.4 | |
(B) XGBoost | Healthy | 97.0 | 91.2 | Healthy | 93.9 | 82.4 |
BS-slight | 92.5 | 90.0 | AN-slight | 87.5 | 80.0 | |
BS-severe | 87.5 | 80.0 | AN-severe | 90.0 | 95.0 | |
Overall | 93.2 | 87.8 | Overall | 90.4 | 85.1 |
Model | Class | Calibration (%) | Prediction | |||||
---|---|---|---|---|---|---|---|---|
Healthy | BS-Slight | BS-Severe | AN-Slight | AN-Severe | Accuracy (%) | |||
SVM | Healthy | 87.9 | 27 | 0 | 3 | 3 | 1 | 79.4 |
BS-slight | 87.5 | 0 | 15 | 5 | 0 | 0 | 75.0 | |
BS-severe | 85 | 1 | 4 | 15 | 0 | 0 | 75.0 | |
AN-slight | 77.5 | 4 | 0 | 0 | 13 | 3 | 65.0 | |
AN-severe | 82.5 | 2 | 2 | 0 | 1 | 15 | 75.0 | |
Overall | 84.1 | 34 | 21 | 23 | 17 | 19 | 74.6 | |
XGBoost | Healthy | 92.4 | 30 | 0 | 0 | 3 | 1 | 88.2 |
BS-slight | 90.0 | 0 | 17 | 2 | 1 | 0 | 85.0 | |
BS-severe | 82.5 | 2 | 3 | 15 | 0 | 0 | 75.0 | |
AN-slight | 77.5 | 4 | 1 | 0 | 13 | 2 | 65.0 | |
AN-severe | 85 | 2 | 0 | 1 | 2 | 15 | 75.0 | |
Overall | 85.5 | 36 | 21 | 17 | 21 | 19 | 78.9 |
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Sun, Y.; Liu, T.; Wang, X.; Hu, Y. Chlorophyll Fluorescence Imaging Combined with Active Oxygen Metabolism for Classification of Similar Diseases in Cucumber Plants. Agronomy 2023, 13, 700. https://doi.org/10.3390/agronomy13030700
Sun Y, Liu T, Wang X, Hu Y. Chlorophyll Fluorescence Imaging Combined with Active Oxygen Metabolism for Classification of Similar Diseases in Cucumber Plants. Agronomy. 2023; 13(3):700. https://doi.org/10.3390/agronomy13030700
Chicago/Turabian StyleSun, Ye, Tan Liu, Xiaochan Wang, and Yonghong Hu. 2023. "Chlorophyll Fluorescence Imaging Combined with Active Oxygen Metabolism for Classification of Similar Diseases in Cucumber Plants" Agronomy 13, no. 3: 700. https://doi.org/10.3390/agronomy13030700