Abstract
Accurate crop yield forecasting is critical for securing food supplies, addressing climate-related risks, and enhancing agricultural production planning. Current predictive models often have drawbacks, including cumbersome architectures, heavy computational load, and insufficient feature mining. To resolve these issues, this study develops a lightweight prediction model designed to reduce structural complexity and computational load while maintaining high accuracy based on a symmetric dual-branch attention mechanism. This model adopts a two-branch symmetric structure: the spatial branch processes remote sensing images and geospatial data via convolutional neural networks to capture crop growth-related spatial patterns effectively. The temporal branch analyzes meteorological and yield time-series data using long short-term memory (LSTM) networks to capture temporal variation trends precisely. The outputs of the two branches are deeply integrated through feature concatenation and an adaptive weighting strategy. To test the model’s performance, this study uses county-level yield records, long-term time series, and meteorological datasets from 1980 to 2018 from major U.S. soybean-producing states as experimental inputs. This dataset is then compared with leading models like AMAP. Results show that in single-year forecasting, the model reduces RMSE by 4.1762% and boosts R2 by 3.4458%—demonstrating strong short-term prediction capability. For five-year long-term forecasting, it reduces the RMSE by 3.2914% and increases the R2 by 4.7537%. This effectively mitigates the performance decline of traditional models in long time-series scenarios, fully leveraging the value of in-depth mining for long-term time-series data.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
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Claims 4
Claim · model-written summary
In single-year forecasting the model reduced RMSE by 4.1762% relative to leading models such as AMAP on US soybean county data.
- Taxon
- soybean
Glycine maxe1 - Outcome
- RMSEe0
- Direction
- reducese0
- Value
- 4.1762%e0
- Comparator
- leading models like AMAPe2
- Setting
- major U.S. soybean-producing statese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in single-year forecasting, the model reduces RMSE by 4.1762% and boosts R2 by 3.4458%”
e1“major U.S. soybean-producing states”
e2“compared with leading models like AMAP”
Claim · model-written summary
In single-year forecasting the model raised R2 by 3.4458% relative to leading models such as AMAP on US soybean county data.
- Taxon
- soybeanGlycine maxe1
- Outcome
- R2e0
- Direction
- boostse0
- Value
- 3.4458%e0
- Comparator
- leading models like AMAPe2
- Setting
- major U.S. soybean-producing statese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“in single-year forecasting, the model reduces RMSE by 4.1762% and boosts R2 by 3.4458%”
e1“major U.S. soybean-producing states”
e2“compared with leading models like AMAP”
Claim · model-written summary
In five-year forecasting the model reduced RMSE by 3.2914% relative to leading models such as AMAP on US soybean county data.
- Taxon
- soybeanGlycine maxe1
- Outcome
- RMSEe0
- Direction
- reducese0
- Value
- 3.2914%e0
- Comparator
- leading models like AMAPe2
- Setting
- major U.S. soybean-producing statese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“For five-year long-term forecasting, it reduces the RMSE by 3.2914% and increases the R2 by 4.7537%”
e1“major U.S. soybean-producing states”
e2“compared with leading models like AMAP”
Claim · model-written summary
In five-year forecasting the model increased R2 by 4.7537% relative to leading models such as AMAP on US soybean county data.
- Taxon
- soybeanGlycine maxe1
- Outcome
- R2e0
- Direction
- increasese0
- Value
- 4.7537%e0
- Comparator
- leading models like AMAPe2
- Setting
- major U.S. soybean-producing statese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“For five-year long-term forecasting, it reduces the RMSE by 3.2914% and increases the R2 by 4.7537%”
e1“major U.S. soybean-producing states”
e2“compared with leading models like AMAP”