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在不同的水和肥料条件下,基于三维光谱和深度学习的三维光谱和深度学习的基础上,溶解有机物演变和草分解率的表征

Guang Yang¹, Hongwei Pan¹, Hongjun Lei¹

¹College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450011, PR China.

Journal of environmental management

|July 5, 2023

概括

监测草分解对于可持续农业至关重要. 这项研究开发了一种使用溶解有机物分析和人工智能的方法,可以在各种条件下准确预测草分解率.

科学领域:

农业科学农业科学
土壤科学土壤科学
环境科学环境科学

背景情况:

草回归是一种可持续的做法,但不完整的分解会损害作物和土壤.
目前缺乏及时监测草分解速度.
溶解有机物 (DOM) 是土壤中草分解的关键指标.

研究的目的:

开发一种及时监测草分解速度的方法.
研究水和肥料处理对草分解的影响.
使用先进的分析技术来预测分解动态.

主要方法:

垃圾袋方法用于监测不同水和肥料方案下的草体重减轻.
三维光光谱 (3D-EEM) 用于分析溶解的有机物成分.
卷积神经网络 (CNN) 与3D-EEM相结合,用于分类和预测分解速度.

主要成果:

在分解过程中,DOM组件从类似蛋白质的转变为类似湿土的.
有机肥和适度的水含量最大限度地提高了土类DOM组件.
在预测草分解率方面,CNN模型取得了高准确性 (R2 = 0.987).

结论:

开发的方法有效地识别了草分解的光谱特征.
根据CNN的预测,在不同的条件下,草的分解率是非常准确的.
这种方法支持有效的草分解和可持续的农业废物管理.

关键词:

卷积神经网络是一种卷积神经网络.频谱分析方法频谱分析方法草分解速度的速度.水和肥料的管理管理.

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