Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Acquisition and Analysis of Data from Remote Sensors Mounted on Unmanned Aerial Vehicles
2.3. Plant Count, Determination of Plant Density, and Estimation of Canopy Cover
2.4. Vegetation Pixels’ Segmentation and Extraction of the Indices Values
2.5. Development and Training of the Feed-Forward Neural Network
2.6. Statistical Analysis
2.7. Variables’ Relative Importance in the Yield Estimation Using Garson’s Algorithm
3. Results and Discussion
3.1. Corn Plant Vegetation Indices
3.2. Plant Density, Canopy Cover, and Yield
3.3. Vegetation Indices and Yield
3.4. Training, Validation, and Testing of the Artificial Neural Network for Estimating Yield
3.5. Variables’ Relative Importance in the Yield Estimation Using Garson’s Algorithm
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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DAS | Development Stage | Date | Sensor | No. of Images | Area (m2) | Ground Sampling Distance (GSD) (cm) |
---|---|---|---|---|---|---|
47 | V8 | 21 May | DJI FC6310 | 272 | 10,067 | 0.49 |
47 | V8 | 21 May | Parrot Sequoia | 752 | 9409 | 2.15 |
79 | R0 | 22 June | DJI FC6310 | 188 | 7720 | 0.49 |
79 | R0 | 22 June | Parrot Sequoia | 752 | 9409 | 2.15 |
Vegetation Index | Formula | Reference |
---|---|---|
Triangular greenness index | TGI = RGreen − 0.39RRed − 0.61RBlue | [48] |
Excess green index | EXG = 2 g − r − b | [49] |
Visible atmospherically resistant index | VARI = (RRed − RGreen)/(RGreen + RRed − RBlue) | [50] |
Normalized difference vegetation index | NDVI = (RNir − RRed)/(RNir + RRed) | [51] |
Normalized difference red edge | NDRE = (RNir − RRE)/(RNir + RRE) | [52] |
Wide dynamic range vegetation index * | WDRVI = (α⋅RNir − RRed)/(α⋅RNir + RRed) | [53] |
DAS | Treatment | Vegetation Index | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
NDVI | NDRE | WDRVI | EXG | TGI | VARI | Yield (g plant−1) | |||||||||
Mean | Std DeV | Mean | Std DeV | Mean | Std DeV | Mean | Std DeV | Mean | Std DeV | Mean | Std DeV | Mean | Std DeV | ||
47 | N140 | 0.46 | 0.05 | 0.19 | 0.02 | −0.55 | 0.04 | 0.40 | 0.03 | 0.40 | 0.03 | 0.12 | 0.02 | 109.3 | 19.6 |
N200 | 0.48 | 0.04 | 0.19 | 0.01 | −0.53 | 0.04 | 0.42 | 0.03 | 0.42 | 0.03 | 0.13 | 0.02 | 134.0 | 2.7 | |
N260 | 0.46 | 0.05 | 0.18 | 0.02 | −0.55 | 0.05 | 0.41 | 0.02 | 0.41 | 0.02 | 0.13 | 0.02 | 135.6 | 2.7 | |
N320 | 0.46 | 0.04 | 0.18 | 0.01 | −0.55 | 0.04 | 0.42 | 0.04 | 0.42 | 0.04 | 0.13 | 0.02 | 137.1 | 3.6 | |
N380 | 0.47 | 0.05 | 0.19 | 0.02 | −0.53 | 0.05 | 0.43 | 0.02 | 0.43 | 0.03 | 0.14 | 0.01 | 138.3 | 5.3 | |
79 | N140 | 0.90 | 0.02 | 0.26 | 0.02 | −0.13 | 0.06 | 0.53 | 0.03 | 0.54 | 0.01 | 0.13 | 0.02 | 109.3 | 19.6 |
N200 | 0.91 | 0.01 | 0.26 | 0.03 | −0.10 | 0.06 | 0.54 | 0.03 | 0.54 | 0.01 | 0.14 | 0.02 | 134.0 | 2.7 | |
N260 | 0.90 | 0.01 | 0.25 | 0.02 | −0.13 | 0.06 | 0.52 | 0.03 | 0.54 | 0.01 | 0.13 | 0.02 | 135.6 | 2.7 | |
N320 | 0.90 | 0.01 | 0.25 | 0.02 | −0.12 | 0.05 | 0.53 | 0.04 | 0.54 | 0.01 | 0.13 | 0.02 | 137.1 | 3.6 | |
N380 | 0.90 | 0.01 | 0.25 | 0.02 | −0.11 | 0.05 | 0.53 | 0.03 | 0.54 | 0.01 | 0.13 | 0.02 | 138.3 | 5.3 |
Input Variables | 47 DAS | 79 DAS | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
R Training | R Validation | R Test | R Total | MAE (t ha−1) | RMSE (t ha−1) | R Training | R Validation | R Test | R Total | MAE (t ha−1) | RMSE (t ha−1) | |
NDVI, NDRE, WDRVI, D, C | 0.96 | 0.97 | 0.98 | 0.97 | 0.285 | 0.414 | 0.96 | 0.97 | 0.98 | 0.96 | 0.242 | 0.337 |
NDVI, NDRE, WDRVI, TGI, EXG, VARI, D, C | 0.98 | 0.93 | 0.98 | 0.97 | 0.307 | 0.400 | 0.97 | 0.90 | 0.99 | 0.97 | 0.249 | 0.425 |
NDRE, D, C | 0.97 | 0.99 | 0.95 | 0.97 | 0.256 | 0.365 | 0.97 | 0.88 | 0.97 | 0.96 | 0.278 | 0.437 |
EXG, D, C | 0.97 | 0.98 | 0.99 | 0.98 | 0.252 | 0.354 | 0.95 | 0.97 | 0.99 | 0.96 | 0.280 | 0.431 |
TGI, EXG, VARI, D, C | 0.97 | 0.94 | 0.96 | 0.96 | 0.331 | 0.470 | 0.92 | 0.97 | 0.96 | 0.94 | 0.292 | 0.562 |
C, D | 0.97 | 0.93 | 0.95 | 0.96 | 0.298 | 0.441 | 0.96 | 0.92 | 0.95 | 0.96 | 0.298 | 0.441 |
NDVI, D, C | 0.96 | 0.99 | 0.93 | 0.97 | 0.280 | 0.425 | 0.96 | 0.94 | 0.91 | 0.96 | 0.300 | 0.449 |
TGI, D, C | 0.96 | 0.97 | 0.98 | 0.97 | 0.304 | 0.414 | 0.94 | 0.95 | 0.97 | 0.95 | 0.312 | 0.459 |
VARI, D, C | 0.97 | 0.99 | 0.93 | 0.97 | 0.279 | 0.395 | 0.97 | 0.93 | 0.90 | 0.95 | 0.347 | 0.571 |
NDVI, NDRE, WDRVI, TGI, EXG, VARI, C | 0.92 | 0.86 | 0.96 | 0.92 | 0.512 | 0.643 | 0.94 | 0.97 | 0.88 | 0.94 | 0.381 | 0.538 |
NDVI, NDRE, WDRVI, TGI, EXG, VARI | 0.80 | 0.94 | 0.92 | 0.86 | 0.622 | 0.809 | 0.84 | 0.91 | 0.90 | 0.86 | 0.528 | 0.876 |
EXG, C | 0.81 | 0.81 | 0.84 | 0.81 | 0.733 | 0.938 | 0.80 | 0.81 | 0.84 | 0.80 | 0.623 | 0.947 |
NDVI, NDRE, WDRVI | 0.74 | 0.65 | 0.85 | 0.73 | 0.811 | 1.093 | 0.82 | 0.85 | 0.87 | 0.85 | 0.641 | 0.837 |
NDVI, C | 0.83 | 0.82 | 0.90 | 0.84 | 0.597 | 0.883 | 0.80 | 0.86 | 0.88 | 0.82 | 0.649 | 0.884 |
VARI, C | 0.86 | 0.94 | 0.51 | 0.85 | 0.604 | 0.836 | 0.86 | 0.79 | 0.92 | 0.86 | 0.653 | 0.917 |
NDVI, NDRE, WDRVI, C | 0.81 | 0.93 | 0.83 | 0.84 | 0.618 | 0.874 | 0.87 | 0.87 | 0.62 | 0.84 | 0.672 | 0.856 |
WDRVI, C | 0.87 | 0.62 | 0.95 | 0.87 | 0.584 | 0.784 | 0.82 | 0.64 | 0.56 | 0.79 | 0.689 | 0.971 |
TGI, EXG, VARI | 0.69 | 0.64 | 0.82 | 0.67 | 0.908 | 1.189 | 0.83 | 0.72 | 0.72 | 0.80 | 0.720 | 0.951 |
TGI, EXG, VARI, C | 0.86 | 0.90 | 0.87 | 0.86 | 0.629 | 0.817 | 0.78 | 0.93 | 0.71 | 0.78 | 0.746 | 1.015 |
WDRVI, D, C | 0.99 | 0.99 | 0.99 | 0.99 | 0.028 | 0.125 | 0.96 | 0.99 | 0.89 | 0.96 | 0.209 | 0.449 |
NDRE, C | 0.75 | 0.98 | 0.97 | 0.81 | 0.527 | 0.986 | 0.68 | 0.68 | 0.55 | 0.65 | 0.774 | 1.179 |
TGI, C | 0.85 | 0.62 | 0.78 | 0.82 | 0.701 | 0.909 | 0.50 | 0.62 | 0.29 | 0.54 | 1.017 | 1.380 |
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García-Martínez, H.; Flores-Magdaleno, H.; Ascencio-Hernández, R.; Khalil-Gardezi, A.; Tijerina-Chávez, L.; Mancilla-Villa, O.R.; Vázquez-Peña, M.A. Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles. Agriculture 2020, 10, 277. https://doi.org/10.3390/agriculture10070277
García-Martínez H, Flores-Magdaleno H, Ascencio-Hernández R, Khalil-Gardezi A, Tijerina-Chávez L, Mancilla-Villa OR, Vázquez-Peña MA. Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles. Agriculture. 2020; 10(7):277. https://doi.org/10.3390/agriculture10070277
Chicago/Turabian StyleGarcía-Martínez, Héctor, Héctor Flores-Magdaleno, Roberto Ascencio-Hernández, Abdul Khalil-Gardezi, Leonardo Tijerina-Chávez, Oscar R. Mancilla-Villa, and Mario A. Vázquez-Peña. 2020. "Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles" Agriculture 10, no. 7: 277. https://doi.org/10.3390/agriculture10070277