Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Sites
2.2. Data Collection
2.2.1. Ground Data Acquisition
2.2.2. UAV Image Acquisition
2.3. Image Processing
2.4. Dataset Creation
2.5. Deep Learning Models
2.5.1. YOLOv5
2.5.2. YOLOv7
2.5.3. CenterNet
2.6. Precision Validation
3. Results
3.1. Model Validation
3.1.1. Model Training and Verification Test Results
3.1.2. Model Training and Verification of Counting Results
3.2. Model Test Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter | Value |
---|---|
UAV weight | 1487 g |
Max flight time | 27 min |
Image max resolution | 1600 × 1300 (4:3.25) |
Time | Train Set | Val (Test) Set | Total |
---|---|---|---|
T1 | 3234 | 1143 | 4377 |
T2 | 3715 | 1235 | 4950 |
T3 | 6284 | 2108 | 8392 |
T4 | 4192 | 1305 | 5497 |
T5 | 4160 | 1408 | 5568 |
T6 | 6770 | 2286 | 9056 |
Total | 28,355 | 9485 | 37,840 |
Model | Time | Training Set | Validation Set | ||||
---|---|---|---|---|---|---|---|
Precision (%) | Recall (%) | F1-Score (%) | Precision (%) | Recall (%) | F1-Score (%) | ||
YOLOv5 | T1 | 93.1 | 93.6 | 93.3 | 93.8 | 88.7 | 91.2 |
T2 | 95.9 | 95.2 | 95.5 | 96.6 | 96.0 | 96.3 | |
T3 | 97.4 | 96.4 | 96.9 | 98.4 | 98.0 | 98.2 | |
T4 | 96.1 | 95.7 | 95.9 | 96.5 | 95.4 | 95.9 | |
T5 | 96.1 | 96.1 | 96.1 | 96.1 | 94.5 | 95.3 | |
T6 | 97.7 | 95.7 | 96.7 | 97.2 | 94.6 | 95.9 | |
YOLOv7 | T1 | 92.3 | 94.4 | 93.3 | 94.3 | 92.0 | 93.2 |
T2 | 96.0 | 96.5 | 96.2 | 95.9 | 96.6 | 96.2 | |
T3 | 97.3 | 98.2 | 97.7 | 98.1 | 98.6 | 98.3 | |
T4 | 96.8 | 96.2 | 96.5 | 96.9 | 96.6 | 96.7 | |
T5 | 95.8 | 96.4 | 96.1 | 97.3 | 96.5 | 96.9 | |
T6 | 98.5 | 97.9 | 98.2 | 97.3 | 96.0 | 96.7 | |
CenterNet | T1 | 89.2 | 79.3 | 83.9 | 85.7 | 79.5 | 82.5 |
T2 | 93.3 | 82.8 | 87.7 | 92.9 | 82.6 | 87.4 | |
T3 | 96.4 | 87.6 | 91.8 | 96.8 | 88.2 | 92.3 | |
T4 | 95.1 | 83.1 | 88.7 | 94.8 | 83.8 | 89.0 | |
T5 | 95.1 | 84.3 | 89.4 | 96.6 | 85.5 | 90.7 | |
T6 | 98.2 | 93.2 | 95.6 | 97.9 | 93.4 | 95.6 |
Model | Time | Training Set | Validation Set | ||||
---|---|---|---|---|---|---|---|
R² | RMSE | RRMSE (%) | R² | RMSE | RRMSE (%) | ||
YOLOv5 | T1 | 0.85 | 9.36 | 8.40 | 0.64 | 12.48 | 11.46 |
T2 | 0.97 | 5.97 | 4.82 | 0.90 | 6.50 | 5.27 | |
T3 | 0.91 | 9.53 | 4.55 | 0.93 | 9.97 | 4.73 | |
T4 | 0.94 | 5.79 | 4.14 | 0.90 | 4.63 | 3.54 | |
T5 | 0.94 | 7.89 | 5.69 | 0.87 | 5.41 | 3.84 | |
T6 | 0.74 | 17.24 | 7.64 | 0.29 | 22.98 | 10.05 | |
YOLOv7 | T1 | 0.88 | 7.25 | 6.50 | 0.65 | 11.10 | 10.20 |
T2 | 0.97 | 11.48 | 9.27 | 0.91 | 5.21 | 4.22 | |
T3 | 0.92 | 8.32 | 3.97 | 0.94 | 8.03 | 3.81 | |
T4 | 0.92 | 5.82 | 4.16 | 0.90 | 4.32 | 3.31 | |
T5 | 0.92 | 8.36 | 6.03 | 0.92 | 5.39 | 3.83 | |
T6 | 0.78 | 9.17 | 4.06 | 0.57 | 11.61 | 5.08 | |
CenterNet | T1 | 0.88 | 10.55 | 9.46 | 0.68 | 12.75 | 11.71 |
T2 | 0.97 | 6.80 | 5.49 | 0.93 | 5.77 | 4.67 | |
T3 | 0.95 | 6.66 | 3.18 | 0.94 | 8.55 | 4.06 | |
T4 | 0.94 | 5.47 | 3.92 | 0.91 | 3.95 | 3.03 | |
T5 | 0.92 | 6.87 | 4.96 | 0.97 | 3.36 | 2.39 | |
T6 | 0.95 | 3.95 | 1.75 | 0.69 | 6.55 | 2.87 |
Model | Training Set | Validation Set | ||||||
---|---|---|---|---|---|---|---|---|
True Value | Predicted Value | False Positive | False Negative | True Value | Predicted Value | False Positive | False Negative | |
YOLOv5 | 4192 | 4334 | 150 | 8 | 1305 | 1339 | 37 | 3 |
YOLOv7 | 4192 | 4324 | 135 | 3 | 1305 | 1334 | 31 | 2 |
CenterNet | 4192 | 4326 | 140 | 6 | 1305 | 1333 | 31 | 3 |
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Feng, Y.; Chen, W.; Ma, Y.; Zhang, Z.; Gao, P.; Lv, X. Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods. Remote Sens. 2023, 15, 2680. https://doi.org/10.3390/rs15102680
Feng Y, Chen W, Ma Y, Zhang Z, Gao P, Lv X. Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods. Remote Sensing. 2023; 15(10):2680. https://doi.org/10.3390/rs15102680
Chicago/Turabian StyleFeng, Yingxiang, Wei Chen, Yiru Ma, Ze Zhang, Pan Gao, and Xin Lv. 2023. "Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods" Remote Sensing 15, no. 10: 2680. https://doi.org/10.3390/rs15102680
APA StyleFeng, Y., Chen, W., Ma, Y., Zhang, Z., Gao, P., & Lv, X. (2023). Cotton Seedling Detection and Counting Based on UAV Multispectral Images and Deep Learning Methods. Remote Sensing, 15(10), 2680. https://doi.org/10.3390/rs15102680