Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices?
Abstract
1. Introduction
2. Methodology
2.1. Study Area
2.2. Data Collection and Processing
2.2.1. Corn Yield Data Collection Processing
2.2.2. Imagery Processing
2.3. Spatial Autocorrelation Evaluation
2.4. Setting up Predictor Sets for Modeling
Vegetation Index | Name | Equation | Ref. |
---|---|---|---|
CREI | Chlorophyll red-edge index | [37] | |
GCI | Green chlorophyll index | [37] | |
NPCI | Normalized pigment chlorophyll index | [38] | |
ARI | Anthocyanin reflectance index | [39] | |
CCCI | Canopy chlorophyll content index | [40] | |
EVI | Enhanced vegetation index | [41] | |
MCARI | Modified chlorophyll absorption in reflectance index (red) | [42] | |
MCCI | Modified chlorophyll content index | ||
NDRE | Normalized difference red-edge index | [40] | |
NG | Normalized green index | [43] | |
BGI | Blue green pigment index | [44] | |
NGRDI | Normalized green red difference index | [45] | |
PPR | Plant pigment ratio | [46] | |
PSRI | Plant senescence reflectance index | [47] | |
TVI | Triangular vegetation index | [48] | |
GNDVI | Green normalized difference vegetation index | [49] | |
MTVI2 | Modified triangular vegetation index (TVI) 2 | [50] | |
NDVI | Normalized difference vegetation index | [51] | |
B-NDVI | Blue normalized difference vegetation index | [52] | |
TCI | Triangular chlorophyl index | [50] | |
MSAVI | Modified soil-adjusted vegetation index | [53] | |
RDVI | Renormalized difference vegetation index | [54] | |
SAVI | Soil-adjusted vegetation index | [41] | |
TrVI | Transformed vegetation index | [51] | |
TSAVI | Transformed soil-adjusted vegetation index | [55] |
2.5. Spatial Regression Modeling
2.5.1. Spatial Lag X Model (SLX)
2.5.2. Machine Learning Models
Random Forest
Extreme Gradient Boosting (XGB)
Extremely Randomized Tree Regression (ET)
Gradient Boosting Regressor (GBR)
2.6. Model Training and Testing
2.6.1. Computation Tools
2.6.2. Hyperparameter Tunning
2.7. Model Performance Evaluation
3. Results
3.1. Spatial Autocorrelation
3.2. Model Performance With and Without Neighbor Information
3.3. Model Performance With and Without Vegetation Indices
3.4. Model Performance Combining Neighborhood Information and Vegetation Indices
3.5. Feature Importance When Combining All the Predictors
3.6. Model Performance Comparison Across Predictor Sets
4. Discussion
4.1. Spatial Autocorrelation of Corn Yield
4.2. Neighborhood Information and Model Performance
4.3. Vegetation Indices and Model Performance
4.4. Model Performance When Combining All Predictors
4.5. Implications for Future Research
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Set | Description | Sub-Set | Details |
---|---|---|---|
Set 1 (S-1) | Spectral bands only (Baseline) | S-1 | Blue (B), green (G), red (R), red-edge (RE), near infrared (NIR) |
Set 2 (S-2) | Spectral bands + spatially lagged bands | S-2A S-2B S-2C S-2D | S-1 + 4 neighbors S-1 + 8 neighbors S-1 + 20 neighbors S-1 + 24 neighbors |
Set 3 (S-3) | Spectral bands + vegetation indices | S-3A | S-1 + CREI |
S-3B | S-3A + GCI | ||
S-3C | S-3B + NPCI | ||
S-3D | S-3C + ARI, CCCI | ||
S-3E | S-3D + EVI, MCARI, MCCI, NDRE, NG | ||
S-3F | S-3E + BGI, NGRDI, PPR, PSRI, TVI | ||
S-3G | S-3F + GNDVI, MTVI2, NDVI, B-NDVI, TCI | ||
S-3H | S-3G + MSAVI, RDVI, SAVI, TrVI, TSAVI | ||
Set 4 (S-4) | Spectral bands + spatially lagged bands + VIs | S-4 | S-2B + 20 VIs |
Model | Hyperparameter | Range or List of Values | Pace Step | Tunned Value |
---|---|---|---|---|
RF | n_estimators | 100–900 | 100 | 500 |
max_depth | 1–40 | 5 | 11 | |
max_features | 1–20 | 5 | 11 | |
XGB | n_estimators | [100, 200, 300, 400] | 200 | |
max_depth | 1–12 | 3 | 7 | |
learning_rate | 0–2 | 0.05 | 0.05 | |
subsample | 0.4–0.8 | 0.1 | 0.7 | |
gamma | 0.1–0.4 | 0.1 | 0.3 | |
colsample_bytree | [0.7, 0.8, 0.9] | 0.9 | ||
ET | n_estimators | 100–900 | 100 | 300 |
max_depth | 1–40 | 5 | 21 | |
max_features | 1–21 | 4 | 21 | |
DT | n_estimators | 100–900 | 100 | 300 |
max_depth | 1–15 | 3 | 12 | |
max_features | 1–21 | 4 | 21 | |
GBR | n_estimators | 100–900 | 100 | 200 |
max_depth | 1–13 | 4 | 5 | |
learning_rate | 0–2 | 0.05 | 0.1 | |
subsample | 0.4–0.8 | 0.1 | 0.7 |
Model | Coefficient of Determination (R2) | Root Mean Square Error (RMSE) | ||||||
---|---|---|---|---|---|---|---|---|
S-1 | Best S-2 | Best S-3 | S-4 | S-1 | Best S-2 | Best S-3 | S-4 | |
LR | 0.19 | 0.48 | 0.31 | 0.46 | 1.28 | 1.03 | 1.19 | 1.05 |
RF | 0.32 | 0.52 | 0.49 | 0.56 | 1.18 | 0.99 | 1.02 | 0.95 |
XGB | 0.41 | 0.54 | 0.52 | 0.57 | 1.10 | 0.97 | 0.99 | 0.94 |
ET | 0.27 | 0.57 | 0.44 | 0.55 | 1.23 | 0.94 | 1.07 | 0.96 |
GBR | 0.39 | 0.50 | 0.48 | 0.50 | 1.12 | 1.01 | 1.04 | 1.01 |
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Noa-Yarasca, E.; Osorio Leyton, J.M.; Hajda, C.B.; Adhikari, K.; Smith, D.R. Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? AI 2025, 6, 58. https://doi.org/10.3390/ai6030058
Noa-Yarasca E, Osorio Leyton JM, Hajda CB, Adhikari K, Smith DR. Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? AI. 2025; 6(3):58. https://doi.org/10.3390/ai6030058
Chicago/Turabian StyleNoa-Yarasca, Efrain, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari, and Douglas R. Smith. 2025. "Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices?" AI 6, no. 3: 58. https://doi.org/10.3390/ai6030058
APA StyleNoa-Yarasca, E., Osorio Leyton, J. M., Hajda, C. B., Adhikari, K., & Smith, D. R. (2025). Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? AI, 6(3), 58. https://doi.org/10.3390/ai6030058