دورية أكاديمية

Estimating Chlorophyll Fluorescence Parameters of Rice (Oryza sativa L.) Based on Spectrum Transformation and a Joint Feature Extraction Algorithm

التفاصيل البيبلوغرافية
العنوان: Estimating Chlorophyll Fluorescence Parameters of Rice (Oryza sativa L.) Based on Spectrum Transformation and a Joint Feature Extraction Algorithm
المؤلفون: Shuangya Wen, Nan Shi, Junwei Lu, Qianwen Gao, Huibing Yang, Zhiqiang Gao
المصدر: Agronomy, Vol 13, Iss 2, p 337 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Agriculture
مصطلحات موضوعية: rice, chlorophyll fluorescence parameters, Fv/Fm, hyperspectral, spectrum transform, correlation coefficient method, Agriculture
الوصف: The chlorophyll fluorescence parameter Fv/Fm plays a significant role in indicating the photosynthetic function of plants. The existing technical methods used to measure Fv/Fm are often inefficient and cumbersome. To realize fast and non-destructive monitoring of Fv/Fm, this study took rice under different fertilizer treatments and measured the hyperspectral reflectance information and Fv/Fm data of rice leaves during the whole growth period. Five spectral transformation methods were used to pre-process the spectral data. Then, spectral characteristic wavelengths were extracted by the correlation coefficient method (CC) combined with the competitive adaptative reweighted sampling (CARS) algorithm. Finally, based on the combination of characteristic wavelengths extracted from different spectral transformations, back propagation neural network (BPNN) models were constructed and evaluated. The results showed that: (1) first derivative transform (FD), multiplicative scatter correction (MSC) and standardized normal variation (SNV) methods could effectively highlight the correlation between spectral data and Fv/Fm. The most sensitive bands with high correlation coefficients were concentrated in the range of 650–850 nm, and the absolute values of the highest correlation coefficients were 0.84, 0.73, and 0.72, respectively. (2) The CC-CARS algorithm could effectively screen the characteristic wavelengths sensitive to Fv/Fm. The number of sensitive bands extracted by FD, MSC, and SNV pre-treatment methods were 14, 13, and 16 which only accounted for 2.33%, 2.16%, and 2.66% of the total spectral wavelength (the number of full spectral bands is 601), respectively. (3) The BPNN models were established based on the above sensitive wavelengths, and it was found that MSC-CC-CARS-BPNN had the highest prediction accuracy, and its testing set R2, RMSE and RPD were 0.74, 1.88% and 2.46, respectively. The results can provide technical references for hyperspectral data pre-processing and rapid and non-destructive monitoring of chlorophyll fluorescence parameters.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2073-4395
Relation: https://www.mdpi.com/2073-4395/13/2/337; https://doaj.org/toc/2073-4395
DOI: 10.3390/agronomy13020337
URL الوصول: https://doaj.org/article/65f4cfe0540c48c38a4ed47970853a33
رقم الأكسشن: edsdoj.65f4cfe0540c48c38a4ed47970853a33
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20734395
DOI:10.3390/agronomy13020337