[1]唐国强,刘梦云,蒋丹垚,等.基于分数阶微分的猕猴桃叶片叶绿素含量估算[J].江苏农业学报,2025,(02):335-344.[doi:doi:10.3969/j.issn.1000-4440.2025.02.014]
　TANG Guoqiang,LIU Mengyun,JIANG Danyao,et al.Estimation of kiwifruit leaf chlorophyll content based on fractional-order differential processing[J].,2025,(02):335-344.[doi:doi:10.3969/j.issn.1000-4440.2025.02.014]
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基于分数阶微分的猕猴桃叶片叶绿素含量估算()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年02期
页码:
335-344
栏目:
农业信息工程
出版日期:
2025-02-28

文章信息/Info

Title:
Estimation of kiwifruit leaf chlorophyll content based on fractional-order differential processing
作者:
唐国强; 刘梦云; 蒋丹垚; 宋正华; 常庆瑞
(西北农林科技大学资源环境学院,陕西杨凌712100)
Author(s):
TANG Guoqiang; LIU Mengyun; JIANG Danyao; SONG Zhenghua; CHANG Qingrui
(College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China)
关键词:
猕猴桃; 叶绿素含量; 高光谱; 分数阶微分变换; 竞争自适应重加权采样
Keywords:
kiwifruit; chlorophyll content; hyperspectral; fractional order differential transformations; competitive adaptive reweighted sampling
分类号:
S663.4
DOI:
doi:10.3969/j.issn.1000-4440.2025.02.014
文献标志码:
A
摘要:
叶片叶绿素含量是表征植被生长状态的重要生理生化参数,传统的叶绿素含量测定方法操作复杂且会破坏叶片组织结构,对植物造成不可逆的损伤。通过构建高精度叶绿素反演模型,可以实现对猕猴桃叶片叶绿素含量的实时无损监测。本研究采集了猕猴桃冠层的高光谱数据,并同步测定了叶片叶绿素相对含量（SPAD值）。通过对原始光谱进行分数阶微分变换(阶数为0～2,步长为0.2),结合竞争自适应重加权采样算法(CARS)筛选得到敏感波段。分别基于原始波段和敏感波段训练随机森林模型(RF)、支持向量机模型(SVR)和极限学习机模型(ELM)。结果表明,分数阶微分变换显著提高了光谱反射率与猕猴桃叶片叶绿素含量的相关性,CARS算法提升了模型精度。光谱反射率经过1.8阶微分处理后,采用CARS算法筛选出敏感波段,利用这些敏感波段训练随机森林模型,取得了最佳效果。训练后的随机森林模型在验证集上决定系数(R2)达到0.93,均方根误差(RMSE)为2.56,相对分析误差(RPD)为3.89。该研究结果可为猕猴桃叶片叶绿素含量的高精度估算提供理论依据和技术参考,对猕猴桃生长监测和精准农业管理具有重要意义。
Abstract:
The leaf chlorophyll content is an important physiological and biochemical parameter for characterizing the growth status of vegetation. Traditional methods for measuring chlorophyll content are cumbersome and destructive to leaf tissue, causing irreversible damage to plants. By constructing high-precision chlorophyll inversion models, real-time, non-destructive monitoring of chlorophyll content in kiwifruit leaves can be achieved. In this study, high-spectral data of the kiwifruit canopy were collected, and the relative chlorophyll content (SPAD) of the leaves was measured synchronously. The original spectra were subjected to fractional-order differential transformation (with orders ranging from 0 to 2 at a step size of 0.2). Subsequently, the competitive adaptive reweighted sampling algorithm (CARS) was used to identify the sensitive bands. Random forest (RF), support vector machine (SVR), and extreme learning machine (ELM) models were trained based on both the original bands and the sensitive bands. The results showed that the fractional-order differential transformation significantly enhanced the correlation between spectral reflectance and chlorophyll content in kiwifruit leaves, and the CARS algorithm improved model accuracy. After the spectral reflectance was processed by the 1.8th order differential, the sensitive bands were screened out by the CARS algorithm, and the random forest model trained using these sensitive bands achieved the best performance. The trained RF model had a determination coefficient (R2) of 0.93, a root mean square error (RMSE) of 2.56, and a relative percent deviation (RPD) of 3.89 on the validation set. The results of this study can provide a theoretical basis and technical reference for the high-precision estimation of chlorophyll content in kiwifruit leaves and are of great significance for kiwifruit growth monitoring and precision agricultural management.

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备注/Memo

备注/Memo:
收稿日期：2024-10-10基金项目：国家自然科学基金项目(41701398、42071240)作者简介：唐国强(2000-),男,湖南衡阳人,硕士研究生,主要从事土地资源与空间信息技术研究。(E-mail)1813377325@qq.com通讯作者：刘梦云,(E-mail)lmy471993@163.com
更新日期/Last Update: 2025-03-27