Dataset Information

Estimating leaf nitrogen concentration based on the combination with fluorescence spectrum and first-derivative.

ABSTRACT: Leaf nitrogen concentration (LNC) is a major indicator in the estimation of the crop growth status which has been diffusely applied in remote sensing. Thus, it is important to accurately obtain LNC by using passive or active technology. Laser-induced fluorescence can be applied to monitor LNC in crops through analysing the changing of fluorescence spectral information. Thus, the performance of fluorescence spectrum (FS) and first-derivative fluorescence spectrum (FDFS) for paddy rice (Yangliangyou 6 and Manly Indica) LNC estimation was discussed, and then the proposed FS + FDFS was used to monitor LNC by multivariate analysis. The results showed that the difference between FS (R 2 = 0.781, s.d. = 0.078) and FDFS (R 2 = 0.779, s.d. = 0.097) for LNC estimation by using the artificial neural network is not obvious. The proposed FS + FDFS can improved the accuracy of LNC estimation to some extent (R 2 = 0.813, s.d. = 0.051). Then, principal component analysis was used in FS and FDFS, and extracted the main fluorescence characteristics. The results indicated that the proposed FS + FDFS exhibited higher robustness and stability for LNC estimation (R 2 = 0.851, s.d. = 0.032) than that only using FS (R 2 = 0.815, s.d. = 0.059) or FDFS (R 2 = 0.801, s.d. = 0.065).

SUBMITTER: Yang J

PROVIDER: S-EPMC7062071 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Estimating leaf nitrogen concentration based on the combination with fluorescence spectrum and first-derivative.

Yang Jian J Du Lin L Gong Wei W Shi Shuo S Sun Jia J

Royal Society open science 20200226 2

Leaf nitrogen concentration (LNC) is a major indicator in the estimation of the crop growth status which has been diffusely applied in remote sensing. Thus, it is important to accurately obtain LNC by using passive or active technology. Laser-induced fluorescence can be applied to monitor LNC in crops through analysing the changing of fluorescence spectral information. Thus, the performance of fluorescence spectrum (FS) and first-derivative fluorescence spectrum (FDFS) for paddy rice (Yangliangy ...[more]

PMID: 32257346

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Project description:Remotely estimating leaf phosphorus concentration (LPC) is crucial for fertilization management, crop growth monitoring, and the development of precision agricultural strategy. This study aimed to explore the best prediction model for the LPC of rice (Oryza sativa L.) using machine learning algorithms fed with full-band (OR), spectral indices (SIs), and wavelet features. To obtain the LPC and leaf spectra reflectance, the pot experiments with four phosphorus (P) treatments and two rice cultivars were carried out in a greenhouse in 2020-2021. The results indicated that P deficiency increased leaf reflectance in the visible region (350-750 nm) and decreased the reflectance in the near-infrared (NIR, 750-1350 nm) regions compared to the P-sufficient treatment. Difference spectral index (DSI) composed of 1080 nm and 1070 nm showed the best performance for LPC estimation in calibration (R2 = 0.54) and validation (R2 = 0.55). To filter and denoise spectral data effectively, continuous wavelet transform (CWT) of the original spectrum was used to improve the accuracy of prediction. The model based on Mexican Hat (Mexh) wavelet function (1680 nm, Scale 6) demonstrated the best performance with the calibration R2 of 0.58, validation R2 of 0.56 and RMSE of 0.61 mg g-1. In machine learning, random forest (RF) had the best model accuracy in OR, SIs, CWT, and SIs + CWT compared with other four algorithms. The SIs and CWT coupling with the RF algorithm had the best results of model validation, the R2 was 0.73 and the RMSE was 0.50 mg g-1, followed by CWT (R2 = 0.71, RMSE = 0.51 mg g-1), OR (R2 = 0.66, RMSE = 0.60 mg g-1), and SIs (R2 = 0.57, RMSE = 0.64 mg g-1). Compared with the best performing SIs based on the linear regression models, the RF algorithm combining SIs and CWT improved the prediction of LPC with R2 increased by 32%. Our results provide a valuable reference for spectral monitoring of rice LPC under different soil P-supplying levels in a large scale.

Dataset Information

Estimating leaf nitrogen concentration based on the combination with fluorescence spectrum and first-derivative.

Publications

Estimating leaf nitrogen concentration based on the combination with fluorescence spectrum and first-derivative.

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