What is the research gap on rice yield prediction?
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There are several research gaps in rice yield prediction. Firstly, there is a lack of knowledge on establishing a widely applicable yield prediction model for different growth environments with varying meteorological factors . Secondly, there is little discussion on the performance of deep learning models, such as LSTM and GRU, in rice yield prediction . Thirdly, there is a need for regression models that combine various characteristic independent variables to accurately simulate the four key factors of rice yield . Lastly, there is a limited availability of high-spatiotemporal-resolution rice yield datasets, which hinders accurate assessment of the impacts of climate change and agricultural production .
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| Papers (5) | Insight |
|---|---|
| The research gap on rice yield prediction is the limited availability of high-spatiotemporal-resolution rice yield datasets over a large region, hindering accurate assessment of climate change impacts and agricultural production simulation. | |
| The research gap on rice yield prediction is the limited availability of high-spatiotemporal-resolution rice yield datasets over a large region, hindering accurate assessment of climate change impacts and agricultural production simulation. | |
| The paper does not explicitly mention a research gap on rice yield prediction. | |
| The research gap on rice yield prediction is the lack of discussion on the performance of LSTM and GRU models in this specific prediction task. | |
| The research gap on rice yield prediction is the lack of knowledge in establishing a widely applicable yield prediction model for different growth environments with different meteorological factors. |
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