Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Details
2.2. Aerial Image Acquisition
2.3. Image Analysis
Pre-Processing and Feature Extraction
2.4. Data Analysis and CGM Estimation
3. Results
3.1. Crop Reflectance and Vegetation Index Feature Evaluation
3.2. Non-Invasive CGM Estimation with ML
3.2.1. Input Feature Selection
3.2.2. Using Reflectance Features as Inputs
3.2.3. Using Reflectance and Vegetation Index Features as Inputs
3.2.4. Impact of Training and Testing Data Split Ratios
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Vegetation Index | Equation | Reference |
---|---|---|
Normalized Difference Vegetation Index (NDVI) | (NIR − R)/(NIR + R) | [25] |
Infrared Percentage Vegetation Index (IPVI) | (NIR)/(NIR + R) | [26] |
Green Normal Difference Vegetation Index (GNDVI) | (NIR − G)/(NIR + G) | [27] |
Green Difference Vegetation Index (GDVI) | NIR − G | [28] |
Enhanced Vegetation Index (EVI) | 2.5 × (NIR − R)/(NIR + 6 × R − 7.5 × B + 1) | [29] |
Leaf Area Index (LAI) | 3.618 × EVI − 0.118 | [30] |
Modified Non-Linear Index (MNLI) | (NIR2 − R) × (1 + L)/(NIR2 + R + L) | [31] |
Soil Adjusted Vegetation Index (SAVI) | 1.5 × (NIR − R)/(NIR + R + 0.5) | [32] |
Optimized Soil Adjusted Vegetation Index (OSAVI) | (NIR − R)/(NIR + R + 0.16) | [33] |
Green Soil Adjusted Vegetation Index (GSAVI) | (NIR − G)/(NIR + G + 0.5) | [32] |
Green Optimized Soil Adjusted Vegetation Index (GOSAVI) | (NIR − G)/(NIR + G + 0.16) | [32] |
Modified Soil Adjusted Vegetation Index (MSAVI2) | (2 × NIR + 1 − sqrt ((2 × NIR + 1) 2 − 8 × (NIR − R)))/2 | [34] |
Normalized Difference Red-edge Index (NDRE) | (NIR − RE)/(NIR + RE) | [35] |
Green Ratio Vegetation Index (GRVI) | NIR/G | [28] |
Green Chlorophyll Index (GCI) | (NIR/G) − 1 | [36] |
Green Leaf Index (GLI) | ((G − R) + (G − B))/((2 × G) + R + B) | [37] |
Simple Ratio (SR) | NIR/R | [38] |
Modified Simple Ratio (MSR) | ((NIR/R) − 1)/(sqrt (NIR/R) + 1) | [39] |
Renormalized Difference Vegetation Index (RDVI) | (NIR − R)/sqrt (NIR + R) | [40] |
Transformed Difference Vegetation Index (TDVI) | 1.5 × ((NIR − R)/sqrt (NIR + R + 0.5)) | [41] |
Visible Atmospherically Resistant Index (VARI) | (G − R)/(G + R − B) | [42] |
Wide Dynamic Range Vegetation Index (WDRVI) | (a × NIR − R)/(a × NIR + R) | [43] |
Vegetation Index | Pearson Correlation (r) |
---|---|
Blue | −0.27 |
Green | 0.05 |
Red | −0.52 |
Red Edge | 0.66 |
Near Infrared | 0.74 |
Normalized Difference Vegetation Index (NDVI) | 0.77 |
Infrared Percentage Vegetation Index (IPVI) | 0.77 |
Green Normal Difference Vegetation Index (GNDVI) | 0.80 |
Difference Vegetation Index (DVI) | 0.76 |
Green Difference Vegetation Index (GDVI) | 0.76 |
Enhanced Vegetation Index (EVI) | 0.77 |
Leaf Area Index (LAI) | 0.77 |
Non-Linear Index (NLI) | 0.78 |
Modified Non-Linear Index (MNLI) | 0.76 |
Soil Adjusted Vegetation Index (SAVI) | 0.77 |
Optimized Soil Adjusted Vegetation Index (OSAVI) | 0.78 |
Green Soil Adjusted Vegetation Index (GSAVI) | 0.78 |
Green Optimized Soil Adjusted Vegetation Index (GOSAVI) | 0.79 |
Modified Soil Adjusted Vegetation Index (MSAVI2) | 0.77 |
Normalized Difference Red-edge Index (NDRE) | 0.76 |
Green Ratio Vegetation Index (GRVI) | 0.79 |
Green Chlorophyll Index (GCI) | 0.79 |
Green Leaf Index (GLI) | 0.69 |
Simple Ratio (SR) | 0.77 |
Modified Simple Ratio (MSR) | 0.78 |
Renormalized Difference Vegetation Index (RDVI) | 0.77 |
Transformed Difference Vegetation Index (TDVI) | 0.78 |
Visible Atmospherically Resistant Index (VARI) | 0.68 |
Wide Dynamic Range Vegetation Index (WDRVI) | 0.78 |
Parameters | Dataset: Entire | Dataset: Test | Dataset: Train | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Train:Test Ratio | Input Group | Best Model | r | rRMSE (%) | Best Model | r | rRMSE (%) | Best Model | r | rRMSE (%) |
50:50 | REFs | RF | 0.86 | 2.14 | SLR | 0.74 | 2.43 | RF | 0.96 | 1.59 |
REFs+VIs | 0.87 | 2.08 | SVM | 0.70 | 2.58 | 0.97 | 1.34 | |||
55:45 | REFs | RF | 0.87 | 2.11 | SLR | 0.74 | 2.51 | RF | 0.96 | 1.47 |
REFs+VIs | 0.87 | 2.08 | SVM | 0.69 | 2.68 | ANN | 0.97 | 1.26 | ||
60:40 | REFs | RF | 0.88 | 2.05 | SLR | 0.70 | 2.27 | RF | 0.96 | 1.47 |
REFs+VIs | 0.88 | 2.03 | SVM | 0.67 | 2.64 | 0.97 | 1.26 | |||
65:35 | REFs | RF | 0.88 | 2.02 | SLR | 0.66 | 2.67 | RF | 0.96 | 1.41 |
REFs+VIs | 0.88 | 2.02 | SVM | 0.64 | 2.78 | 0.97 | 1.22 | |||
70:30 | REFs | RF | 0.89 | 1.95 | SLR | 0.61 | 2.76 | RF | 0.96 | 1.43 |
REFs+VIs | 0.89 | 1.93 | SVM | 0.60 | 2.92 | 0.97 | 1.21 | |||
75:25 | REFs | RF | 0.89 | 1.92 | ANN | 0.62 | 2.82 | RF | 0.96 | 1.35 |
REFs+VIs | 0.90 | 1.86 | SVM | 0.60 | 3.08 | 0.97 | 1.17 | |||
80:20 | REFs | RF | 0.91 | 1.86 | PLSR | 0.65 | 2.70 | RF | 0.96 | 1.34 |
REFs+VIs | 0.92 | 1.73 | SLR | 0.71 | 2.74 | 0.96 | 1.21 | |||
85:15 | REFs | RF | 0.93 | 1.69 | PLSR | 0.62 | 2.82 | RF | 0.96 | 1.33 |
REFs+VIs | 0.92 | 1.65 | SLR | 0.67 | 2.69 | 0.97 | 1.20 | |||
90:10 | REFs | RF | 0.94 | 1.55 | PLSR | 0.43 | 2.91 | RF | 0.96 | 1.32 |
REFs+VIs | 0.94 | 1.45 | SLR | 0.51 | 2.84 | 0.97 | 1.16 | |||
95:5 | REFs | RF | 0.94 | 1.51 | KNN | 0.69 | 3.25 | RF | 0.96 | 1.31 |
REFs+VIs | 0.95 | 1.37 | SLR | 0.77 | 2.59 | 0.97 | 1.17 |
Variable | p Value (r) | p Value (rRMSE) |
---|---|---|
Model | <0.001 | <0.001 |
Train–test split | <0.001 | 0.619 |
Dataset | <0.001 | <0.001 |
Input group | 0.374 | 0.725 |
Train–test split: Dataset | <0.001 | <0.001 |
Train–test split: Input group | 0.189 | 0.290 |
Dataset: Input group | 0.450 | 0.002 |
Train–test split: Dataset: Input group | 0.204 | 0.544 |
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Jjagwe, P.; Chandel, A.K.; Langston, D. Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques. Land 2023, 12, 2188. https://doi.org/10.3390/land12122188
Jjagwe P, Chandel AK, Langston D. Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques. Land. 2023; 12(12):2188. https://doi.org/10.3390/land12122188
Chicago/Turabian StyleJjagwe, Pius, Abhilash K. Chandel, and David Langston. 2023. "Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques" Land 12, no. 12: 2188. https://doi.org/10.3390/land12122188
APA StyleJjagwe, P., Chandel, A. K., & Langston, D. (2023). Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques. Land, 12(12), 2188. https://doi.org/10.3390/land12122188