Abstract
Leaf nitrogen concentration (LNC) is a significant indicator of crops growth status, which is related to crop yield and photosynthetic efficiency. Laser-induced fluorescence is a promising technology for LNC estimation and has been widely used in remote sensing. The accuracy of LNC monitoring relies greatly on the selection of fluorescence characteristics and the number of fluorescence characteristics. It would be useful to analyze the performance of fluorescence intensity and ratio characteristics at different wavelengths for LNC estimation. In this study, the fluorescence spectra of paddy rice excited by different excitation light wavelengths (355 nm, 460 nm, and 556 nm) were acquired. The performance of the fluorescence intensity and fluorescence ratio of each band were analyzed in detail based on back-propagation neural network (BPNN) for LNC estimation. At 355 nm and 460 nm excitation wavelengths, the fluorescence characteristics related to LNC were mainly located in the far-red region, and at 556 nm excitation wavelength, the red region being an optimal band. Additionally, the effect of the number of fluorescence characteristics on the accuracy of LNC estimation was analyzed by using principal component analysis combined with BPNN. Results demonstrate that at least two fluorescence spectral features should be selected in the red and far-red regions to estimate LNC and efficiently improve the accuracy of LNC estimation.
Highlights
Chlorophyll is an essential factor in crop photosynthesis, and nitrogen (N), a main element in chlorophyll, can favorably affect the growth and quality of crops
3.2.TLoNaaCnnaEallsyytzizmeeattthhieoenppBreraedsdeidcitcoitvniveFealubaoiblrietlsyictyeonfoctefhSethpfeelcutfloraureosrceesncceencsepescptreactfroar fmoronmitonriintogriLnNgCLiNnCpaidndpyardicdey, trhicee,BtPhNeNBPaNlgNoriatlhgmoriwthams uwsaeds utoseidnvtoerisnevlyerpserelydipctreLdNicCt LbNasCedbaosnedthoenfltuhoereflsucoenrecsecesnpceectsrpa.ecTthrae. rTehlaetrieolnTasotihoainpnsabhleiyptzwebetethwneetpehrneetdmhiectamisvueearaesbudirlaeintdydaonpfdrtehpderiecfdtleuicdotreLedNsLcCeNnwCceewrsepereeesctetarsabtalfiboslhrisemhdeoadnnaidtnodirlilniulglsutLsrtaNrtaeCtdeidinninpFaiFgdiguduryerer4i4.c.e, the back-propagation neural network (BPNN) algorithm was used to inversely predict Leaf nitrogen concentration (LNC) based on the fluorescence spectra
The performance of the fluorescence characteristics and fluorescence ratio of each band for the LNC estimation were analyzed in detail based on the BPNN model combined with principal component analysis (PCA)
Summary
Chlorophyll is an essential factor in crop photosynthesis, and nitrogen (N), a main element in chlorophyll, can favorably affect the growth and quality of crops. Estimating leaf nitrogen concentration (LNC) accurately and nondestructively is important for the accurate diagnosis and quality evaluation of plant growth status [3,4]. The development of remote sensing has made it a significant tool for monitoring plant growth at the leaf, canopy, and landscape levels [5,6,7,8]. Many researchers have investigated hyperspectral remote sensing and found a certain difference among the sensitive bands of the LNC for different crops [9,10,11]. The optimal bands will vary at different growth stages of the same crops [12]. Chlorophyll fluorescence was proposed and utilized for monitoring crop growth status. Chlorophyll fluorescence has shown to be a promising technology for monitoring crop growth status
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