一种基于变压器的方法,用于早期预测大豆产量,使用时间序列图像
Luning Bi1, Owen Wally2, Guiping Hu1
1Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States.
Frontiers in plant science
|July 6, 2023
概括
本研究介绍了一种基于变压器的模型,用于使用早期图像和种子数据准确预测作物产量. 这种新的方法显著减少了预测错误,改善了精确农业决策.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 精准农业依赖于准确的作物产量预测,以有效管理.
- 传统的方法是劳动密集型和耗时的.
- 现有的基于图像的预测模型在与远程依赖性作斗争.
研究的目的:
- 用早期图像和种子信息开发基于变压器的作物产量预测方法.
- 与传统方法相比,提高产量预测的准确性和效率.
主要方法:
- 图像细分成植物和土壤类别.
- 使用两个视觉变压器 (ViT) 模块进行特征提取.
- 使用变压器模块进行时间序列特征处理.
- 图像和种子特征的整合用于产量估计.
主要成果:
- 与基线模型相比,拟议的基于变压器的方法将预测误差降低了40%以上.
- 分析了种子信息对预测准确性的影响.
- 种子信息对于预测低产量至关重要.
结论:
- 基于变压器的模型为作物产量预测提供了一种优越的方法.
- 集成种子信息可以提高预测的准确性,特别是在低产量的场景中.
- 该方法在推进精密农业方面具有重大潜力.
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