从高光谱数据得出的光谱-频率转换结合深度学习估算水稻叶绿素含量

Q2 Agricultural and Biological Sciences
Lei Du, Shanjun Luo
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引用次数: 0

摘要

作为水稻光合作用的重要色素,叶绿素含量与生长状况和光合能力密切相关。通过估算叶绿素含量,可以监测水稻的生长状况,便于进行精确的田间管理,如施肥和灌溉。高光谱遥感技术的发展使无损、快速、有效地估算叶绿素含量成为可能,为管理和监测大面积水稻生长提供了技术支持。虽然高光谱数据具有精细的光谱分辨率,但也会产生大量的信息冗余和噪声。本研究主要针对预测水稻叶绿素含量时输入变量不稳定、估算模型在不同时期适用性差等问题进行研究。通过引入谐波分析理论和时频转换方法,提出了基于小波包变换-一阶微分-谐波分析(WPT-FD-HA)的深度神经网络(DNN)模型框架,避免了频谱参数计算的不确定性。比较了基于 WPT-FD 和 WPT-FD-HA 变量估测水稻叶绿素含量在苗期、分蘖期、拔节期、头粒期、籽粒灌浆期、乳熟期和完全期的准确性,以评价所建议框架的有效性和普适性。结果表明,所有 WPT-FD-HA 模型的单期验证精度的决定系数 (R2) 值均大于 0.9,均方根误差 (RMSE) 值均小于 1。即使采用独立的数据分割验证,多期模型精度仍可达到 R2 = 0.95 和 RMSE = 1.4。基于 WPT-FD-HA 的深度学习框架表现出很强的稳定性。本研究的成果值得用于利用高光谱数据大范围监测水稻生长。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spectral-Frequency Conversion Derived from Hyperspectral Data Combined with Deep Learning for Estimating Chlorophyll Content in Rice
As a vital pigment for photosynthesis in rice, chlorophyll content is closely correlated with growth status and photosynthetic capacity. The estimation of chlorophyll content allows for the monitoring of rice growth and facilitates precise management in the field, such as the application of fertilizers and irrigation. The advancement of hyperspectral remote sensing technology has made it possible to estimate chlorophyll content non-destructively, quickly, and effectively, offering technical support for managing and monitoring rice growth across wide areas. Although hyperspectral data have a fine spectral resolution, they also cause a large amount of information redundancy and noise. This study focuses on the issues of unstable input variables and the estimation model’s poor applicability to various periods when predicting rice chlorophyll content. By introducing the theory of harmonic analysis and the time-frequency conversion method, a deep neural network (DNN) model framework based on wavelet packet transform-first order differential-harmonic analysis (WPT-FD-HA) was proposed, which avoids the uncertainty in the calculation of spectral parameters. The accuracy of estimating rice chlorophyll content based on WPT-FD and WPT-FD-HA variables was compared at seedling, tillering, jointing, heading, grain filling, milk, and complete periods to evaluate the validity and generalizability of the suggested framework. The results demonstrated that all of the WPT-FD-HA models’ single-period validation accuracy had coefficients of determination (R2) values greater than 0.9 and RMSE values less than 1. The multi-period validation model had a root mean square error (RMSE) of 1.664 and an R2 of 0.971. Even with independent data splitting validation, the multi-period model accuracy can still achieve R2 = 0.95 and RMSE = 1.4. The WPT-FD-HA-based deep learning framework exhibited strong stability. The outcome of this study deserves to be used to monitor rice growth on a broad scale using hyperspectral data.
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来源期刊
Agriculture
Agriculture Agricultural and Biological Sciences-Horticulture
CiteScore
1.90
自引率
0.00%
发文量
4
审稿时长
11 weeks
期刊介绍: The Agriculture (Poľnohospodárstvo) is a peer-reviewed international journal that publishes mainly original research papers. The journal examines various aspects of research and is devoted to the publication of papers dealing with the following subjects: plant nutrition, protection, breeding, genetics and biotechnology, quality of plant products, grassland, mountain agriculture and environment, soil science and conservation, mechanization and economics of plant production and other spheres of plant science. Journal is published 4 times per year.
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