Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Satellite Imagery
2.3. Data Processing and Building the Dataset
2.3.1. Yield Data
2.3.2. Topographical Data
2.3.3. Dataset Extraction and Feature Importance
2.4. Auto-ML
2.5. Model Performance Analysis
2.6. Theoretical Framework
3. Results
3.1. Descriptive Statistics of Corn Yield, Spectral Bands, TWI, and TPI
3.2. Feature Importance
3.3. Auto-ML for Predicting Corn Yield Using TWI, TPI, and Spectral Bands
3.4. Comparison of Models Using Different Features in Three Scenarios in Terms of Accuracy, Relative Error, and Tendency
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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ML Algorithm | Features | Reference | Model Level |
---|---|---|---|
Deep neural network | NDVI, EVI, and temperature | [32] | Country |
Recurrent neural network and convolutional neural network | MODIS reflectance (MOD091A), Weather data and soil property data | [33] | County |
Deep neural network | Genotype, weather, and soil | [34] | Hybrid locations |
Ordinary least-square considering spatial correlation | NDRE, NDVI, and GDVI | [35] | Field |
Year | ||
---|---|---|
2018 | 2019 | |
Sowing | 10 April | 27 March |
Tasseling | 23 June | 9 June |
Harvest | 3 September | 29 August |
Corn hybrid | Dekalb® DKC 66-97 | Dekalb® DKC 66-97 |
Row spacing | 0.76 m | 0.76 m |
Plant population | 84,000 pl/ha−1 | 84,000 pl/ha−1 |
Management Zone | Soil Type | Sand% | Silt% | Clay% |
---|---|---|---|---|
MZ1 1 | Silty clay | 13.07 | 40.13 | 46.80 |
MZ2 2 | Clay loam | 23.20 | 45.47 | 31.33 |
MZ3 3 | Clay loam | 34.40 | 31.60 | 34.00 |
2018 | 2019 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
MZ1 | |||||||||||
mean | Std | C.V(%) | min | max | mean | std | C.V(%) | min | max | ||
Blue | 5509 | 72 | 1 | 5172 | 6189 | Blue | 499 | 27 | 5 | 410 | 723 |
Green | 4773 | 58 | 1 | 4554 | 5398 | Green | 588 | 32 | 5 | 504 | 819 |
Red | 3238 | 85 | 3 | 2959 | 4380 | Red | 557 | 52 | 9 | 443 | 909 |
NIR | 11,441 | 290 | 3 | 8590 | 12,435 | NIR | 3721 | 159 | 4 | 2983 | 4210 |
TWI | 6 | 1 | 20 | 2 | 10 | TWI | 6 | 1 | 18 | 2 | 10 |
TPI | −1 | 1 | −158 | −5 | 5 | TPI | −1 | 1 | −151 | −5 | 5 |
Yield | 13.25 | 1.28 | 9 | 5.31 | 17.49 | Yield | 14.19 | 1.14 | 8 | 2.89 | 19.77 |
MZ2 | |||||||||||
Blue | 5576 | 99 | 2 | 5231 | 6904 | Blue | 529 | 35 | 7 | 382 | 702 |
Green | 4857 | 104 | 2 | 4567 | 5897 | Green | 626 | 42 | 7 | 473 | 818 |
Red | 3368 | 168 | 5 | 3031 | 5101 | Red | 627 | 79 | 13 | 412 | 981 |
NIR | 11,126 | 423 | 4 | 5476 | 12,717 | NIR | 3686 | 180 | 5 | 1962 | 4277 |
TWI | 4 | 1 | 23 | 2 | 9 | TWI | 4 | 1 | 23 | 2 | 8 |
TPI | 0 | 2 | 413 | −9 | 6 | TPI | 1 | 2 | 309 | −9 | 6 |
Yield | 11.37 | 1.68 | 15 | 3.97 | 17.75 | Yield | 13.05 | 1.68 | 13 | 2.35 | 19.44 |
MZ3 | |||||||||||
Blue | 5513 | 79 | 1 | 5176 | 6965 | Blue | 510 | 26 | 5 | 431 | 696 |
Green | 4784 | 68 | 1 | 4574 | 6136 | Green | 604 | 31 | 5 | 507 | 808 |
Red | 3255 | 96 | 3 | 3005 | 5453 | Red | 579 | 50 | 9 | 453 | 979 |
NIR | 11,366 | 273 | 2 | 9093 | 12,499 | NIR | 3715 | 121 | 3 | 3146 | 4210 |
TWI | 6 | 1 | 15 | 1 | 9 | TWI | 6 | 1 | 15 | 1 | 9 |
TPI | 0 | 1 | −2218 | −3 | 4 | TPI | 0 | 1 | −2081 | −3 | 4 |
Yield | 13.05 | 1.14 | 9 | 1.41 | 17.96 | Yield | 13.99 | 1.08 | 8 | 3.23 | 19.17 |
2018 | 2019 | All Years | |||||||
---|---|---|---|---|---|---|---|---|---|
Whole Field | |||||||||
Model Type | Training | Test | Model Type | Training | Test | Model Type | Training | Test | |
Bands | SE | 0.83 | 0.85 | XGBoost | 0.78 | 0.8 | SE | 0.81 | 0.84 |
Bands + TWI | SE | 0.4 | 0.73 | SE | 0.47 | 0.73 | SE | 0.64 | 0.76 |
Bands + TPI | SE | 0.44 | 0.75 | SE | 0.45 | 0.73 | SE | 0.63 | 0.77 |
Bands + TPI + TWI | SE | 0.5 | 0.65 | SE | 0.42 | 0.66 | SE | 0.57 | 0.68 |
MZ1 | |||||||||
Bands | GBM | 0.73 | 0.76 | GBM | 0.67 | 0.69 | GBM | 0.7 | 0.74 |
Bands + TWI | SE | 0.49 | 0.64 | SE | 0.39 | 0.62 | SE | 0.36 | 0.63 |
Bands + TPI | SE | 0.19 | 0.62 | SE | 0.3 | 0.62 | XGBoost | 0.36 | 0.63 |
Bands + TPI + TWI | SE | 0.1 | 0.47 | SE | 0.16 | 0.52 | SE | 0.19 | 0.5 |
MZ2 | |||||||||
Bands | SE | 0.83 | 0.88 | XGBoost | 0.91 | 0.97 | SE | 0.85 | 0.92 |
Bands + TWI | SE | 0.38 | 0.75 | SE | 0.44 | 0.85 | SE | 0.45 | 0.79 |
Bands + TPI | SE | 0.4 | 0.74 | SE | 0.41 | 0.85 | SE | 0.46 | 0.78 |
Bands + TPI + TWI | SE | 0.15 | 0.59 | SE | 0.22 | 0.72 | SE | 0.45 | 0.68 |
MZ3 | |||||||||
Bands | GBM | 0.72 | 0.73 | GBM | 0.63 | 0.64 | GBM | 0.67 | 0.68 |
Bands + TWI | XGBoost | 0.38 | 0.64 | XGBoost | 0.32 | 0.59 | XGBoost | 0.31 | 0.61 |
Bands + TPI | SE | 0.25 | 0.62 | XGBoost | 0.33 | 0.59 | XGBoost | 0.32 | 0.6 |
Bands + TPI + TWI | SE | 0.11 | 0.48 | XGBoost | 0.25 | 0.51 | SE | 0.19 | 0.49 |
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Oliveira, M.F.d.; Ortiz, B.V.; Morata, G.T.; Jiménez, A.-F.; Rolim, G.d.S.; Silva, R.P.d. Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction. Remote Sens. 2022, 14, 6171. https://doi.org/10.3390/rs14236171
Oliveira MFd, Ortiz BV, Morata GT, Jiménez A-F, Rolim GdS, Silva RPd. Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction. Remote Sensing. 2022; 14(23):6171. https://doi.org/10.3390/rs14236171
Chicago/Turabian StyleOliveira, Mailson Freire de, Brenda Valeska Ortiz, Guilherme Trimer Morata, Andrés-F Jiménez, Glauco de Souza Rolim, and Rouverson Pereira da Silva. 2022. "Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction" Remote Sensing 14, no. 23: 6171. https://doi.org/10.3390/rs14236171
APA StyleOliveira, M. F. d., Ortiz, B. V., Morata, G. T., Jiménez, A. -F., Rolim, G. d. S., & Silva, R. P. d. (2022). Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction. Remote Sensing, 14(23), 6171. https://doi.org/10.3390/rs14236171