Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Design
2.2. UAV Image Acquisition and Preprocessing
2.3. Cotton Ground Data Collection
2.4. UAV Multi-Source Remote Sensing Image Alignment
2.5. UAV Multispectral Image Segmentation Methods
2.5.1. Vegetation Index Threshold Segmentation Method
2.5.2. Image Segmentation Based on SVM Supervised Classification
2.5.3. Cotton Abundance Information Extraction Based on Mixed Pixel Decomposition
2.6. Vegetation Indices Calculation from UAV Multispectral Imagery
2.7. Cotton SPAD Inversion Model Selection
2.8. Cotton SPAD Inversion Model Accuracy Evaluation Indexes
3. Results and Analysis
3.1. Accuracy Analysis of Different Segmentation Methods for UAV Multispectral Images
3.2. Correlation Analysis between Vegetation Indices and Cotton SPAD
3.3. Cotton SPAD Inversion Model Construction
3.4. SPAD Spatiotemporal Mapping Based on Optimal Inversion Modeling
4. Discussions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Nitrogen Fertilizer Gradient | Number of Cotton SPAD Observed | Mean | Max | Min | SD | CV (%) |
---|---|---|---|---|---|---|
N1 region | 20 | 46.94 | 51.1 | 42.0 | 2.58 | 5.49 |
N2 region | 20 | 50.86 | 54.8 | 46.3 | 2.40 | 4.71 |
N3 region | 20 | 49.62 | 53.3 | 44.2 | 2.44 | 4.91 |
whole region | 60 | 49.13 | 54.8 | 42 | 3.16 | 6.43 |
Altitude | Mixed Pixels % | Cotton Pixels % | Soil Pixels % | Shadow Pixels % |
---|---|---|---|---|
30 m | 58.2% | 23.5% | 14.2% | 4.1% |
50 m | 66.7% | 17.4% | 12.1% | 3.8% |
80 m | 76.4% | 11.1% | 9.6% | 2.9% |
Vegetation Index | Calculation Formula | Reference |
---|---|---|
Atmospheric resistant vegetation index (ARVI) | / | [31] |
Chlorophyll vegetation index (CVI) | [32] | |
Normalized difference vegetation index (NDVI) | [33] | |
Green normalized difference vegetation index (GNDVI) | )/() | [34] |
Meris terrestrial chlorophyll index (MTCI) | )/() | [35] |
Rededge normalized difference vegetation index (RENDVI) | )/( + REG) | [36] |
Modified chlorophyll absorption reflectance index (MCARI) | [37] | |
Leaf chlorophyll index (LCI) | [38] |
Segmentation Method | 30 m | 50 m | 80 m | |||
---|---|---|---|---|---|---|
Cotton Proportion% | Average Error% | Cotton Proportion% | Average Error% | Cotton Proportion% | Average Error% | |
VIT | 64.34 | 2.95% | 66.74 | 1.97% | 72.51 | 7.05% |
65.57 | 70.26 | 76.83 | ||||
66.84 | 71.34 | 77.39 | ||||
SVM | 69.72 | 2.14% | 70.75 | 3.83% | 63.62 | 5.61% |
70.75 | 72.94 | 62.22 | ||||
71.53 | 73.38 | 62.91 | ||||
SMA | 65.83 | 2.85% | 66.25 | 3.62% | 64.31 | 4.82% |
64.68 | 63.97 | 61.69 | ||||
66.52 | 64.52 | 65.14 | ||||
MESMA | 67.14 | 1.74% | 66.36 | 2.55% | 65.82 | 3.61% |
65.52 | 65.14 | 63.31 | ||||
67.70 | 66.43 | 65.63 |
Model | Segmentation Method | Training | Validation | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE | MAE | ||
PLSR | All pix | 0.682 | 1.65 | 1.36 | 0.637 | 1.59 | 1.43 |
VIT | 0.725 | 1.53 | 1.24 | 0.679 | 1.51 | 1.30 | |
SVM | 0.748 | 1.45 | 1.13 | 0.706 | 1.43 | 1.15 | |
SMA | 0.738 | 1.47 | 1.18 | 0.688 | 1.56 | 1.42 | |
MESMA | 0.753 | 1.43 | 1.17 | 0.701 | 1.46 | 1.18 | |
FR | All pix | 0.725 | 1.52 | 1.27 | 0.662 | 1.62 | 1.36 |
VIT | 0.757 | 1.42 | 1.11 | 0.703 | 1.51 | 1.22 | |
SVM | 0.774 | 1.37 | 1.16 | 0.722 | 1.45 | 1.17 | |
SMA | 0.767 | 1.39 | 1.16 | 0.696 | 1.53 | 1.31 | |
MESMA | 0.779 | 1.35 | 1.12 | 0.728 | 1.43 | 1.16 | |
SVR | All pix | 0.768 | 1.31 | 1.05 | 0.734 | 1.45 | 1.22 |
VIT | 0.823 | 1.13 | 0.91 | 0.772 | 1.35 | 1.13 | |
SVM | 0.841 | 1.07 | 0.76 | 0.794 | 1.31 | 1.08 | |
SMA | 0.837 | 1.10 | 0.85 | 0.786 | 1.32 | 1.11 | |
MESMA | 0.849 | 1.05 | 0.67 | 0.810 | 1.27 | 1.04 |
Model | Segmentation Method | Training | Validation | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE | MAE | ||
PLSR | All pix | 0.668 | 1.74 | 1.38 | 0.635 | 1.69 | 1.50 |
VIT | 0.691 | 1.54 | 1.25 | 0.648 | 1.65 | 1.44 | |
SVM | 0.707 | 1.50 | 1.26 | 0.646 | 1.67 | 1.41 | |
SMA | 0.712 | 1.49 | 1.26 | 0.659 | 1.56 | 1.34 | |
MESMA | 0.728 | 1.52 | 1.24 | 0.672 | 1.52 | 1.32 | |
FR | All pix | 0.689 | 1.58 | 1.29 | 0.633 | 1.76 | 1.55 |
VIT | 0.712 | 1.51 | 1.19 | 0.655 | 1.67 | 1.38 | |
SVM | 0.754 | 1.45 | 1.16 | 0.685 | 1.55 | 1.42 | |
SMA | 0.740 | 1.46 | 1.14 | 0.696 | 1.57 | 1.31 | |
MESMA | 0.769 | 1.32 | 1.01 | 0.718 | 1.53 | 1.26 | |
SVR | All pix | 0.718 | 1.53 | 1.16 | 0.682 | 1.62 | 1.33 |
VIT | 0.74 | 1.46 | 1.12 | 0.699 | 1.57 | 1.25 | |
SVM | 0.768 | 1.31 | 0.95 | 0.727 | 1.51 | 1.22 | |
SMA | 0.773 | 1.25 | 0.96 | 0.744 | 1.48 | 1.21 | |
MESMA | 0.809 | 1.21 | 0.93 | 0.778 | 1.46 | 1.16 |
Model | Segmentation Method | Training | Validation | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE | MAE | ||
PLSR | All pix | 0.606 | 1.80 | 1.41 | 0.565 | 1.86 | 1.62 |
VIT | 0.614 | 1.87 | 1.43 | 0.534 | 1.94 | 1.67 | |
SVM | 0.651 | 1.68 | 1.35 | 0.608 | 1.77 | 1.65 | |
SMA | 0.695 | 1.62 | 1.34 | 0.641 | 1.65 | 1.58 | |
MESMA | 0.714 | 1.57 | 1.22 | 0.663 | 1.54 | 1.48 | |
FR | All pix | 0.619 | 1.82 | 1.41 | 0.554 | 1.82 | 1.68 |
VIT | 0.626 | 1.75 | 1.45 | 0.568 | 1.78 | 1.53 | |
SVM | 0.667 | 1.66 | 1.40 | 0.591 | 1.75 | 1.51 | |
SMA | 0.687 | 1.60 | 1.25 | 0.605 | 1.72 | 1.49 | |
MESMA | 0.732 | 1.48 | 1.16 | 0.614 | 1.72 | 1.45 | |
SVR | All pix | 0.663 | 1.63 | 1.32 | 0.591 | 1.88 | 1.61 |
VIT | 0.671 | 1.64 | 1.37 | 0.604 | 1.78 | 1.48 | |
SVM | 0.695 | 1.55 | 1.28 | 0.625 | 1.74 | 1.50 | |
SMA | 0.721 | 1.52 | 1.23 | 0.672 | 1.63 | 1.45 | |
MESMA | 0.740 | 1.46 | 1.12 | 0.697 | 1.58 | 1.27 |
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Tian, B.; Yu, H.; Zhang, S.; Wang, X.; Yang, L.; Li, J.; Cui, W.; Wang, Z.; Lu, L.; Lan, Y.; et al. Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition. Agriculture 2024, 14, 1452. https://doi.org/10.3390/agriculture14091452
Tian B, Yu H, Zhang S, Wang X, Yang L, Li J, Cui W, Wang Z, Lu L, Lan Y, et al. Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition. Agriculture. 2024; 14(9):1452. https://doi.org/10.3390/agriculture14091452
Chicago/Turabian StyleTian, Bingquan, Hailin Yu, Shuailing Zhang, Xiaoli Wang, Lei Yang, Jingqian Li, Wenhao Cui, Zesheng Wang, Liqun Lu, Yubin Lan, and et al. 2024. "Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition" Agriculture 14, no. 9: 1452. https://doi.org/10.3390/agriculture14091452