Migrating from Invasive to Noninvasive Techniques for Enhanced Leaf Chlorophyll Content Estimations Efficiency

Kandpal, Kishor Chandra; Kumar, Amit

doi:10.1080/10408347.2023.2188425

Cited by 1 publication

(1 citation statement)

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“…Kandpal, K.C. and colleagues compared various methods of chlorophyll detection in leaves under laboratory and field conditions, concluding that Arnon's spectrophotometric method is most suitable for laboratory settings, while machine learning methods are widely employed in chlorophyll detection tasks based on hyperspectral data [9]. Zhao, J.W.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Zhou,

Li,

Wang

et al. 2024

Plants

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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.

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Section: Introductionmentioning

confidence: 99%