Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach
Abstract
:1. Introduction
2. Materials
2.1. Experimental Site and Crop Yield Records
2.2. Satellite-Derived Vegetation Indices and Meteorological Variables
2.3. Data Preprocessing
3. Methodology
4. Experiments and Results
4.1. Experiment Setup
4.2. Evaluation Results
5. Discussion
5.1. Weighting Mechanism Analysis
5.2. t-SNE Visualization
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Domain | Environment and Climate | # Samples | Land Cover Layer | Variables |
---|---|---|---|---|
Eastern Temperate Forests (ETFs) | Largely covered by closed-canopy deciduous forests with a humid and temperate climate. | Corn: 5650 Soybean: 5658 | USDA-NASS Cropland Data Layer (CDL) |
|
Great Plains (GPs) | Comparatively low biodiversity with a hot summer and low rainfall. | Corn: 5599 Soybean: 5229 |
Year | Experiment | RF | DNN | ADANN | PDANN | ||||
---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | R2 | RMSE | ||
2019 | GP → ETF | 0.33 | 1.13 | 0.37 | 1.09 | 0.53 | 0.94 | 0.56 | 0.91 |
ETF → GP | 0.70 | 1.28 | 0.68 | 1.31 | 0.73 | 1.20 | 0.78 | 1.10 | |
2020 | GP → ETF | 0.52 | 1.06 | 0.59 | 1.28 | 0.67 | 0.89 | 0.71 | 0.83 |
ETF → GP | 0.54 | 1.67 | 0.64 | 1.46 | 0.75 | 1.22 | 0.77 | 1.18 | |
2021 | GP → ETF | 0.53 | 1.08 | 0.49 | 1.17 | 0.59 | 1.01 | 0.65 | 0.94 |
ETF → GP | 0.75 | 1.67 | 0.71 | 1.80 | 0.74 | 1.70 | 0.75 | 1.67 |
Year | Experiment | RF | DNN | ADANN | PDANN | ||||
---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | R2 | RMSE | ||
2019 | GP → ETF | 0.01 | 0.46 | 0.31 | 0.39 | 0.50 | 0.33 | 0.53 | 0.31 |
ETF → GP | 0.69 | 0.35 | 0.65 | 0.39 | 0.74 | 0.34 | 0.74 | 0.34 | |
2020 | GP → ETF | 0.27 | 0.20 | 0.50 | 0.33 | 0.56 | 0.31 | 0.65 | 0.28 |
ETF → GP | 0.67 | 0.44 | 0.62 | 0.47 | 0.68 | 0.43 | 0.72 | 0.40 | |
2021 | GP → ETF | 0.42 | 0.42 | 0.53 | 0.39 | 0.56 | 0.37 | 0.60 | 0.35 |
ETF → GP | 0.75 | 0.53 | 0.64 | 0.64 | 0.74 | 0.54 | 0.79 | 0.49 |
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Ma, Y.; Yang, Z.; Huang, Q.; Zhang, Z. Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach. Remote Sens. 2023, 15, 4562. https://doi.org/10.3390/rs15184562
Ma Y, Yang Z, Huang Q, Zhang Z. Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach. Remote Sensing. 2023; 15(18):4562. https://doi.org/10.3390/rs15184562
Chicago/Turabian StyleMa, Yuchi, Zhengwei Yang, Qunying Huang, and Zhou Zhang. 2023. "Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach" Remote Sensing 15, no. 18: 4562. https://doi.org/10.3390/rs15184562