Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

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外文摘要:Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.
外文关键词:hyperspectral; fluorescence spectrum; rice; non-destructive testing
作者:Zhou, Ju; Li, Feiyi; Wang, Xinwu; Yin, Heng; Zhang, Wenjing; Du, Jiaoyang; Pu, Haibo
作者单位:Sichuan Agr Univ; Chongqing Yitong Univ
期刊名称:PLANTS-BASEL
期刊影响因子:0.0
出版年份:2024
出版刊次:13(9)
原文传递申请:江苏省科技资源(工程技术文献)统筹服务平台

  1. 编译服务:智慧农业
  2. 编译者:虞德容
  3. 编译时间:2025-01-03