Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing †
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. Datasets
3.2. Yield Prediction Model
3.3. Hyper3DNetReg Architecture
3.4. Predicted Yield Map Generation
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Field | # Samples 1st Year | # Samples 2nd Year | # Samples 3rd Year | Observed Years |
---|---|---|---|---|
F1 | 408 | 316 | 317 | 2016, 2018, 2020 |
G1 | 484 | 497 | 614 | 2016, 2018, 2020 |
G2 | 1014 | 920 | 1014 | 2016, 2019, 2021 |
G3 | 290 | 414 | — | 2017, 2020 |
Field | Split | Training + Validation | Test |
---|---|---|---|
F1 | A | 2016, 2018 | 2020 |
B | 2016, 2020 | 2018 | |
C | 2018, 2020 | 2016 | |
G1 | A | 2016, 2018 | 2020 |
B | 2016, 2020 | 2018 | |
C | 2018, 2020 | 2016 | |
G2 | A | 2016, 2019 | 2021 |
B | 2016, 2021 | 2019 | |
C | 2019, 2021 | 2016 | |
G3 | A | 2017 | 2020 |
B | 2020 | 2017 |
Layer Name | Kernel Size | Padding Size | Output Size |
---|---|---|---|
Input | — | — | (5, 5, n, 1) |
Conv3D + ReLU + BN | (3, 3, 3) | (1, 1, 1) | (5, 5, n, 32) |
Conv3D + ReLU + BN | (3, 3, 3) | (1, 1, 1) | (5, 5, n, 32) |
CONCAT | — | — | (5, 5, n, 64) |
Conv3D + ReLU + BN | (3, 3, 3) | (1, 1, 1) | (5, 5, n, 32) |
CONCAT | — | — | (5, 5, n, 96) |
Conv3D + ReLU + BN | (3, 3, 3) | (1, 1, 1) | (5, 5, n, 32) |
CONCAT | — | — | (5, 5, n, 128) |
Reshape | — | — | (5, 5, ) |
Dropout (0.5) | — | — | (5, 5, ) |
SepConv2D + ReLU + BN | (3, 3) | (1, 1) | (5, 5, 512) |
SepConv2D + ReLU + BN | (3, 3) | (1, 1) | (5, 5, 320) |
Dropout (0.5) | — | — | (5, 5, 320) |
SepConv2D + ReLU + BN | (3, 3) | (1, 1) | (5, 5, 256) |
Dropout (0.5) | — | — | (5, 5, 256) |
SepConv2D + ReLU + BN | (3, 3) | (1, 1) | (5, 5, 128) |
SepConv2D + ReLU + BN | (3, 3) | (1, 1) | (5, 5, 32) |
if or : | |||
Conv2D + ReLU | (3, 3) | (N, N, 1) | |
elif : | |||
Conv2D + ReLU | (3, 3) | (0, 0) | (3, 3, 1) |
Reshape | — | — | (9, 1) |
FC | — | — | N |
Field | Metric | Hyper3D NetReg N = 1 | Hyper3D NetReg N = 3 | Hyper3D NetReg N = 5 | AdaBoost. App N = 1 | SAE N = 1 | 3D-CNN N = 1 | CNN-LF N = 1 | RF N = 1 | BMLR N = 1 | MLR N = 1 |
---|---|---|---|---|---|---|---|---|---|---|---|
F1 | 13.52 | 11.88 | 10.88 | 12.69 | 10.93 | 11.64 | 10.73 | 15.45 | 10.95 | 10.98 | |
8.94 | 8.10 | 7.01 | 7.74 | 6.75 | 7.59 | 7.42 | 11.42 | 6.75 | 6.74 | ||
r | 0.15 | 0.47 | 0.50 | 0.16 | 0.27 | 0.26 | 0.30 | 0.37 | 0.52 | 0.51 | |
33.58 | 36.21 | 43.2 | 39.74 | 41.66 | 38.85 | 40.96 | 38.96 | 40.37 | 40.39 | ||
51.87 | 56.29 | 62.23 | 58.82 | 60.99 | 57.81 | 60.15 | 57.83 | 59.74 | 59.8 | ||
G1 | 17.39 | 15.93 | 15.19 | 16.05 | 15.66 | 18.94 | 16.81 | 18.36 | 16.82 | 16.85 | |
10.09 | 10.63 | 9.04 | 9.92 | 10.19 | 11.24 | 10.70 | 11.81 | 10.01 | 10.06 | ||
r | 0.29 | 0.34 | 0.43 | 0.29 | 0.33 | 0.32 | 0.37 | 0.26 | 0.37 | 0.37 | |
15.93 | 15.44 | 15.95 | 19.73 | 22.21 | 22.35 | 23.15 | 17.4 | 24.48 | 24.51 | ||
34.96 | 36.45 | 37.01 | 40.56 | 44.0 | 44.17 | 46.03 | 37.28 | 46.58 | 46.61 | ||
G2 | 17.05 | 19.47 | 16.71 | 17.02 | 24.81 | 24.52 | 21.1 | 16.97 | 27.6 | 28.09 | |
11.55 | 15.72 | 12.45 | 12.66 | 37.12 | 18.20 | 17.70 | 14.01 | 20.31 | 20.92 | ||
r | 0.37 | 0.29 | 0.55 | 0.43 | 0.10 | 0.10 | 0.29 | 0.60 | 0.21 | 0.19 | |
5.44 | 5.52 | 6.55 | 5.92 | 1.73 | 3.3 | 5.56 | 8.59 | 2.03 | 1.88 | ||
13.96 | 12.69 | 15.38 | 14.43 | 5.59 | 8.9 | 13.51 | 18.97 | 3.69 | 3.35 | ||
G3 | 19.28 | 19.11 | 16.36 | 23.19 | 17.62 | 21.44 | 42.86 | 18.34 | 23.9 | 27.57 | |
14.51 | 13.75 | 11.26 | 16.71 | 13.32 | 13.73 | 14.88 | 11.98 | 15.35 | 19.19 | ||
r | 0.35 | 0.54 | 0.64 | 0.52 | 0.58 | 0.31 | 0.30 | 0.45 | 0.56 | 0.55 | |
25.36 | 27.22 | 29.49 | 25.2 | 27.81 | 27.37 | 23.5 | 23.83 | 26.25 | 23.12 | ||
48.24 | 51.43 | 54.05 | 48.32 | 52.55 | 47.79 | 37.78 | 47.12 | 45.32 | 39.61 |
Field | Metric | Hyper3D NetReg N = 1 | Hyper3D NetReg N = 3 | Hyper3D NetReg N = 5 | AdaBoost. App N = 1 | SAE N = 1 | 3D-CNN N = 1 | CNN-LF N = 1 | RF N = 1 | BMLR N = 1 | MLR N = 1 |
---|---|---|---|---|---|---|---|---|---|---|---|
F1 | 19.04 | 18.46 | 19.34 | 14.98 | 21.41 | 20.43 | 17.83 | 19.15 | 14.42 | 14.94 | |
14.27 | 15.18 | 16.96 | 9.74 | 20.43 | 8.40 | 9.47 | 16.73 | 5.96 | 5.80 | ||
r | 0.19 | 0.18 | 0.21 | 0.15 | 0.18 | 0.27 | 0.26 | 0.14 | 0.19 | 0.19 | |
26.54 | 29.12 | 28.63 | 30.01 | 28 | 32.14 | 29.42 | 30.34 | 30.68 | 30.58 | ||
57.24 | 60.47 | 59.98 | 63.35 | 57.67 | 66.5 | 64.68 | 59.62 | 66.36 | 66.28 | ||
G1 | 23.82 | 26.41 | 27.01 | 24.34 | 19.04 | 33.03 | 23.24 | 19.19 | 19.62 | 20.42 | |
14.96 | 17.98 | 20.53 | 17.57 | 12.96 | 26.88 | 13.95 | 12.92 | 13.22 | 14.01 | ||
r | 0.35 | 0.27 | 0.41 | 0.41 | 0.30 | 0.33 | 0.19 | 0.53 | 0.43 | 0.43 | |
16.38 | 18.39 | 14.64 | 22.51 | 25.47 | 24.97 | 23.67 | 28.88 | 26.72 | 26.64 | ||
42 | 41.16 | 36.88 | 46.27 | 52.16 | 45.13 | 46.2 | 57.19 | 54.86 | 54.72 | ||
G2 | 43.42 | 43.36 | 46.49 | 43.6 | 46.64 | 44.92 | 41.22 | 39.6 | 35.68 | 35.4 | |
37.74 | 36.47 | 40.04 | 37.09 | 40.48 | 38.18 | 34.35 | 32.88 | 27.71 | 27.34 | ||
r | 0.17 | 0.21 | 0.23 | 0.03 | 0.04 | 0.28 | 0.18 | 0.24 | 0.03 | 0.03 | |
9.69 | 8.96 | 10.01 | 7.46 | 6 | 9.09 | 9.32 | 12.39 | 9.74 | 9.34 | ||
21.33 | 20.72 | 20.21 | 19.06 | 15.95 | 19.51 | 21.6 | 25.71 | 24.87 | 24.38 | ||
G3 | 21.79 | 18.51 | 18.09 | 21.51 | 19.39 | 18.6 | 21.6 | 21.97 | 17.96 | 27.76 | |
13.53 | 11.57 | 11.53 | 14.55 | 12.02 | 11.08 | 14.13 | 16.35 | 11.44 | 22.08 | ||
r | 0.47 | 0.49 | 0.53 | 0.57 | 0.57 | 0.51 | 0.40 | 0.47 | 0.57 | 0.58 | |
32.09 | 37.19 | 41.85 | 40.65 | 40.52 | 35.34 | 26.88 | 37.08 | 44.17 | 39.5 | ||
54.94 | 60.31 | 64.63 | 62.64 | 62.39 | 60.47 | 50.08 | 60.74 | 66.72 | 59.6 |
Field | Metric | Hyper3D NetReg N = 1 | Hyper3D NetReg N = 3 | Hyper3D NetReg N = 5 | AdaBoost. App N = 1 | SAE N = 1 | 3D-CNN N = 1 | CNN-LF N = 1 | RF N = 1 | BMLR N = 1 | MLR N = 1 |
---|---|---|---|---|---|---|---|---|---|---|---|
F1 | 23.62 | 20.97 | 19.39 | 17.86 | 16.95 | 19.85 | 15.13 | 13.28 | 13.43 | 13.13 | |
21.49 | 19.02 | 17.44 | 13.93 | 14.48 | 17.35 | 11.85 | 9.85 | 10.57 | 10.21 | ||
r | 0.25 | 0.32 | 0.34 | 0.27 | 0.24 | 0.28 | 0.24 | 0.29 | 0.27 | 0.27 | |
19.27 | 19.72 | 22.27 | 20.89 | 20.26 | 19.64 | 19.86 | 21.97 | 22.0 | 21.51 | ||
42.15 | 46.16 | 49.2 | 45.96 | 47.77 | 45.04 | 46.61 | 50.69 | 50.6 | 50.11 | ||
G1 | 16.85 | 14.31 | 12.88 | 16.85 | 14.12 | 14.88 | 18.72 | 16.27 | 18.13 | 20.43 | |
10.82 | 9.43 | 8.57 | 11.51 | 9.82 | 10.35 | 12.30 | 11.51 | 12.29 | 13.33 | ||
r | 0.23 | 0.24 | 0.31 | 0.21 | 0.15 | 0.26 | 0.29 | 0.11 | 0.12 | 0.11 | |
16.17 | 17.68 | 20.01 | 17.27 | 17.11 | 18.35 | 16.22 | 17.75 | 15.54 | 14.5 | ||
37 | 43.91 | 46.91 | 39.62 | 42.47 | 42.25 | 35.64 | 40.86 | 36.2 | 31.21 | ||
G2 | 18.05 | 17.22 | 16.66 | 16.17 | 18.09 | 16.69 | 22.83 | 30.69 | 16.81 | 16.84 | |
11.24 | 11.78 | 10.48 | 11.70 | 12.50 | 11.56 | 18.72 | 23.04 | 11.67 | 11.65 | ||
r | 0.21 | 0.37 | 0.41 | 0.05 | 0.08 | 0.10 | 0.38 | 0.10 | 0.18 | 0.18 | |
4.93 | 8.64 | 7.39 | 5.37 | 2.5 | 6.18 | 7.57 | 4.82 | 5.72 | 5.75 | ||
12.86 | 17.91 | 16.98 | 14.05 | 10.11 | 14.18 | 16.95 | 12.93 | 15.16 | 15.2 |
Split | 1st Year | 2nd Year | 3rd Year |
---|---|---|---|
F1 | 86 | 130 | 101 |
G1 | 82.35 | 199.5 | 66.1 |
G2 | 78.9 | 92.6 | 60.8 |
G3 | 66 | 105.5 | — |
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Morales, G.; Sheppard, J.W.; Hegedus, P.B.; Maxwell, B.D. Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing. Sensors 2023, 23, 489. https://doi.org/10.3390/s23010489
Morales G, Sheppard JW, Hegedus PB, Maxwell BD. Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing. Sensors. 2023; 23(1):489. https://doi.org/10.3390/s23010489
Chicago/Turabian StyleMorales, Giorgio, John W. Sheppard, Paul B. Hegedus, and Bruce D. Maxwell. 2023. "Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing" Sensors 23, no. 1: 489. https://doi.org/10.3390/s23010489
APA StyleMorales, G., Sheppard, J. W., Hegedus, P. B., & Maxwell, B. D. (2023). Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing. Sensors, 23(1), 489. https://doi.org/10.3390/s23010489