Deep Learning Model for Estimating Winter Wheat Yield
Author Information
Author(s): Zhang Lei, Li Changchun, Wu Xifang, Xiang Hengmao, Jiao Yinghua, Chai Huabin
Primary Institution: Henan Polytechnic University
Hypothesis
Can a deep learning model integrating multisource remote sensing data improve the accuracy of winter wheat yield estimation?
Conclusion
The BO-CNN-BiLSTM model can reliably estimate winter wheat yields, providing valuable insights for agricultural policymaking.
Supporting Evidence
- The BCBL model achieved an R² of 0.81 and RMSE of 616.99 kg/ha.
- SIF data significantly improved yield estimation accuracy across different models.
- The model reliably identified critical stages of winter wheat yield formation.
- Yield estimates were consistent with official statistics.
- The model performed well even under varying climatic conditions.
Takeaway
This study created a smart computer program that helps farmers know how much wheat they will grow, using special satellite pictures and weather data.
Methodology
The study developed a BO-CNN-BiLSTM model that combines convolutional neural networks and bidirectional LSTM to analyze remote sensing and climate data for yield estimation.
Potential Biases
Potential bias may arise from the reliance on specific datasets that may not represent all climatic conditions.
Limitations
The model's performance may vary across different regions due to differences in feature distributions.
Participant Demographics
The study focused on winter wheat yields in Henan Province, China.
Statistical Information
P-Value
0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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