植物营养与肥料学报2025，Vol.31Issue(11)：2252-2265,14.DOI:10.11674/zwyf.2025038
优选小波函数提高马铃薯叶片叶绿素的高光谱估算精度
Optimizing wavelet functions to improve the accuracy of hyperspectral estimation of chlorophyll in potato leaves
摘要
Abstract
[Objectives]The leaf chlorophyll content(LCC)is the most commonly used index for timely,rapid and non-destructive estimation of crop growth and yield.The purpose of this study was to establish the best estimation model for LCC spectral monitoring,and to provide technical support for rapid and non-destructive monitoring of potato growth information,so as to achieve accurate management of potato production.[Methods]From 2023 to 2024,field experiments on potato nitrogen application rates were conducted using five varieties across six locations.Hyperspectral reflectance data and SPAD values of potato leaves were measured during both the pre-flowering and post-flowering stages,while corresponding chlorophyll content was determined using spectrophotometry.Based on two chlorophyll-sensitive spectral index algorithms-the selected double difference index(DD)and the triangular chlorophyll index(TCI)-optimized spectral indices were established using a band optimization algorithm to analyze the correlation between measured chlorophyll content and the spectral indices as well as SPAD values.Subsequently,108 functions from 15 continuous wavelet families and 126 functions from 7 discrete wavelet families were calculated to screen for the optimal wavelet function and wavelet features.The optimal wavelet function was applied to estimate leaf chlorophyll content(LCC)from extensive field experimental data,and the model was evaluated using independent experiments.[Results]The DD index exhibited the strongest correlation with LCC(R2=0.62),followed by TCI(R2=0.58),while SPAD showed the lowest correlation(R2=0.50).This indicated that the spectral indices correlated better with LCC than SPAD does,and that DD outperforms TCI.The continuous wavelet transform(CWT)demonstrated the optimal performance at the 4th decomposition scale,with the LCC estimation model producing R2 values ranging from 0.2 to 0.7,whereas the discrete wavelet transform(DWT)performed best at the 1st scale,yielding LCC models with R2 values concentrated around 0.48,indicating that CWT provides superior accuracy in estimating chlorophyll content in potato leaves compared to DWT.Among the CWTs,the sym family demonstrated the best performance,with sym15 achieving the highest R2 value of 0.61.The spectrum of wavelets strongly correlated with LCC were predominantly concentrated within 450-900 nm,and the spectrum 629 nm,630 nm,and 716 nm showed the most significant wavelet features at the fourth scale.The optimal wavelet was sym15(W630,S4),which was located in the 4th scale and the red-light region,and the independent verification results of the model showed that compared with the model based on the double-difference index and SPAD,the model based on the wavelet feature sym15(R2=0.73-0.80,RMSE=0.22-0.32 mg/g)performed best in LCC prediction.[Conclusions]This study identified sym15(W630,S4)as the optimal wavelet function and feature for constructing a potato LCC estimation model,providing an effective method for the accurate estimation of the potato leaf chlorophyll content.关键词
马铃薯/叶片叶绿素含量/高光谱/小波函数/小波特征Key words
potato/chlorophyll content in leaves/hyperspectral/wavelet function/wavelet characteristics引用本文复制引用
李巧娜,李斐,杨海波..优选小波函数提高马铃薯叶片叶绿素的高光谱估算精度[J].植物营养与肥料学报,2025,31(11):2252-2265,14.基金项目
国家自然科学基金项目(32160757) （32160757）
中央引导地方科技发展资金(2024ZY0104) （2024ZY0104）
内蒙古自治区自然科学基金项目(2024QN03012) （2024QN03012）
内蒙古自治区直属高校基本科研业务费(BR240103). （BR240103）
