使用植物电生理学数据应用的深度学习技术,检测由缺乏引起的压力
Daniel González I Juclà1,2, Elena Najdenovska3, Fabien Dutoit1
1School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland, 1401, Yverdon-les-Bains, Switzerland.
Scientific reports
|June 14, 2023
概括
深度学习准确地检测电生理学信号的植物压力,改善了对番茄植物中缺乏的早期检测. 这种方法为农业实践提供了可持续的进步.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 计算生物学 计算生物学
背景情况:
- 植物电生理学是评估植物健康的有希望的工具.
- 分析电生理数据的传统方法是计算密集型的,简化了原始信号.
- 深度学习 (DL) 提供了自动化的特征学习,但在电生理学中未被充分利用用于植物应激检测.
研究的目的:
- 将深度学习技术应用于原始电生理学数据,以检测番茄植物中的缺乏应激.
- 与现有方法相比,评估DL模型的准确性和效率.
- 探索农业环境中早期压力检测的潜力.
主要方法:
- 在典型的生产条件下利用来自16棵番茄植物的原始电生理记录.
- 应用深度学习模型用于植物压力状态的自动分类.
- 使用预测准确度和信心水平评估模型性能.
主要成果:
- 实现了大约88%的初始预测准确度,用于检测植物压力.
- 通过结合预测可信度,准确度提高到96%以上.
- 在当前最先进的状态上表现出优越的性能,精度超过8%.
- 在早期阶段成功检测到压力.
结论:
- 深度学习有效地分析原始植物电生理学数据以检测压力.
- 拟议的DL方法为识别缺乏的准确性和效率提供了显著的改进.
- 这种方法在生产环境中具有直接适用性,为自动化和可持续的农业实践铺平了道路.
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