在植物育种实验中整合多模式遥感,深度学习和注意力机制,用于产量预测
Claudia Aviles Toledo1, Melba M Crawford1,2, Mitchell R Tuinstra2
1Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States.
Frontiers in plant science
|August 9, 2024
概括
这项研究引入了使用堆叠长期短期记忆 (LSTM) 网络和多式远程传感数据进行准确的玉米谷物产量预测的深度学习模型. 模型 模型的模型
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
背景情况:
- 对人工智能在植物育种和作物管理中的解释性至关重要.
- 深度学习模型,特别是堆叠的长短期记忆 (LSTM) 网络,显示出对收益率预测的希望.
- 整合多模式遥感数据可以增强人工智能模型的能力.
研究的目的:
- 探索和评估堆叠的LSTM深度学习网络,用于玉米谷物产量预测.
- 适应这些网络用于多模式遥感数据,包括高光谱图像,LiDAR和环境数据.
- 研究注意力机制在将产量结果归因于遗传和环境因素方面的解释性.
主要方法:
- 开发了一种多模式的深度学习架构,将高光谱图像,LiDAR点云和环境数据同化.
- 纳入了注意力机制,以权衡不同数据模式和时间特征的重要性.
- 分析了注意力权重的解释性,与生物生长阶段和产量预测相关.
主要成果:
- 提出的基于注意力的模型实现了玉米产量的高预测准确度,R值在0.82到0.93.9之间.
- 发现注意力权重与已知的生物生长阶段一致,表明生物可解释的特征.
- 该研究表明,该模型能够识别关键生长阶段和影响作物产量的因素.
结论:
- 基于注意力的多模式深度学习架构有效预测玉米谷物产量,并提供可解释的见解.
- 该模型的可解释性支持其在植物育种和作物管理中的应用,以获得可操作的见解.
- 这项研究突出了将各种遥感数据与深度学习相结合的潜力,以促进农业科学的发展.
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