ArticleFood science & nutrition2023
Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR.
Article in Food science & nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Modeling and Visualization of Nitrogen and Chlorophyll in GreenhousePlants (Basel, Switzerland) · 2025Article
- Vis/NIR Spectroscopy and Chemometrics for Non-Destructive Estimation of Chlorophyll Content in Different Plant Leaves.Sensors (Basel, Switzerland) · 2025Article
- Visible-near infrared hyperspectral imaging for non-destructive estimation of leaf nitrogen content under water-saving irrigation in protected tomato cultivation.Frontiers in plant science · 2025Article
- Assessment of Peanut Protein Powder Quality by Near-Infrared Spectroscopy and Generalized Regression Neural Network-Based Approach.Foods (Basel, Switzerland) · 2024Article
- Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data.Polymers · 2024Article
- Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra.Forestry research · 2024Article
- Qualitative and Quantitative Assessments of Apple Quality Using Vis Spectroscopy Combined with Improved Particle-Swarm-Optimized Neural Networks.Foods (Basel, Switzerland) · 2023Article
- Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR.Food science & nutrition · 2023Article
- Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy.Foods (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Visible-near-infrared spectroscopy is known for its rapid and nondestructive characteristics designed to predict leaf chlorophyll content (LCC) of winter wheat. It is believed that the nonlinear technique is preferable to the linear method. The canopy reflectance was applied to generate the LCC prediction model. To accomplish such an objective, artificial neural networks (ANN), along with partial least squares regression (PLSR), nonlinear, and linear evaluation methods have been employed and evaluated to predict wheat LCC. The wheat leaves reflectance spectra were initially preprocessed using Savitzky-Golay smoothing, differentiation (first derivative), SNV (Standard Normal Variate), MSC (Multiplicative Scatter Correction), and their combinations. Afterward, a model for LCC using the reflectance spectra was developed by means of the PLS and ANN. The vis/NIR spectroscopy samples at the 350-1400 nm wavelength were preprocessed using S. Golay smoothing, D
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