一个可解释的Bi-LSTM模型用于冬季小麦产量预测.
Abhasha Joshi1, Biswajeet Pradhan1, Subrata Chakraborty1,2
1Centre for Advanced Modeling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering & IT, University of Technology Sydney, Ultimo, NSW, Australia.
Frontiers in plant science
|February 3, 2025
概括
这项研究引入了可解释的深度学习,用于作物产量预测. 双向长期短期记忆 (Bi-LSTM) 模型准确预测冬季小麦产量,识别植被指数和天气等关键因素,以改善农业管理.
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:
- 准确的作物产量预测对于粮食安全和农业管理至关重要.
- 深度学习 (DL) 模型,特别是长短期记忆 (LSTM) 网络,显示出有希望但缺乏可解释性.
- 在作物产量预测中,DL的区域范围的应用和理解尚未得到充分探索.
研究的目的:
- 开发和实施一个可解释的深度学习模型,用于准确预测作物产量.
- 为了比较LSTM,1D卷积神经网络 (1D-CNN) 和双向长期短期记忆 (Bi-LSTM) 模型的性能.
- 利用可解释性技术来理解这些DL模型的决策过程.
主要方法:
- 开发和评估了三个连续的深度学习模型:LSTM,1D-CNN和Bi-LSTM.
- 应用了三个可解释性技术:局部可解释模型不可知解释 (LIME),集成梯度 (IG) 和沙普利增量解释 (SHAP).
- 分析的特征对于预测冬季小麦产量的重要性.
主要成果:
- 双LSTM模型实现了最高的预测性能 (R2高达0.88) 和通用性.
- 可解释性分析确定了后期生长阶段的增强植被指数 (EVI),温度和降水作为冬季小麦产量的关键因素.
- 证明了可解释的人工智能 (XAI) 在理解模型预测,识别高/低收益率实例和诊断错误方面的实用性.
结论:
- 可解释的Bi-LSTM模型为准确可靠的作物产量预测提供了强大的工具.
- XAI方法为模型行为提供了关键的见解,增强了信任,并使有针对性的农业干预成为可能.
- 这种方法通过将预测准确性与透明度相结合,促进了DL在农业中的应用.
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