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在一个多维的HSI世界中确保可解释性和维度减少,用于早期XAI诊断植物压力

Maxim Lysov¹, Konstantin Pukhkiy¹, Evgeny Vasiliev¹

¹Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhny Novgorod, Russia.

Entropy (Basel, Switzerland)

|May 27, 2023

概括

本研究介绍了一种可解释的人工智能 (XAI) 模型,用于通过整合超光谱 (HSI) 和热红外 (TIR) 成像来早期检测植物应激. 该模型有效地从HSI数据中预测植物温度,从而能够在小麦中快速诊断干旱压力.

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:

早期发现植物压力对于作物管理和粮食安全至关重要.
整合多个数据源可以提高诊断准确性.
可解释的人工智能 (XAI) 提供了对复杂模型的可解释性见解.

研究的目的:

开发一个XAI模型用于早期诊断植物压力,特别是小麦的干旱压力.
将高光谱成像 (HSI) 和热红外 (TIR) 数据结合起来,用于增强植物应激检测.
创建一个可解释的模型,利用HSI和TIR数据的优势.

主要方法:

使用单层感知子 (SLP) 回归器开发了一个新的XAI模型.
该模型将HSI的植物光谱特征与TIR的温度数据集成在一起.
在25天的实验中,使用HSI和TIR摄像头生成了一个定制的数据集.

主要成果:

该研究确定HSI通道143 (820nm) 与TIR数据的相关性最高.
对于植物温度预测,XAI模型实现了0.2-0.3°C的RMSE,适用于早期诊断.
培训所需的HSI通道数量减少了25-30倍 (从204到7-8),而不会影响准确度.
该模型展示了计算效率高的训练,在不到一分钟的时间内完成.

结论:

开发的R-XAI模型有效地整合了HSI和TIR数据,用于可解释的植物应激诊断.
这种方法使知识从TIR领域转移到减少的一组HSI道.
该模型显示了在精准农业和作物监测中实际应用的巨大潜力.

关键词:

早期诊断早期诊断早期诊断可解释的人工智能超光谱图像的使用植物压力是植物的压力.热红外训练热红外训练热红外训练零射击学习的学习

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