ArticlePlants (Basel, Switzerland)2020
Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms.
Article in Plants (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A Condition-Aware Shading Domain-Adaptive Framework for Robust Chlorophyll Inversion Across Shade Managements inPlants (Basel, Switzerland) · 2026Article
- Hyperspectral estimation of leaf chlorophyll under small-sample conditions via spectral augmentation and weighted ensemble learning.Frontiers in plant science · 2026Article
- Estimation of chlorophyll content in cotton leaves using UAV RGB imagery.Frontiers in plant science · 2026Article
- Robust retrieval of biophysical traits under shade gradients in youngFrontiers in plant science · 2026Article
- Estimation of the Relative Chlorophyll Content of Pear Leaves Based on Field Spectrometry in Alaer, Xinjiang.Sensors (Basel, Switzerland) · 2025Article
- Exploring Microelement Fertilization and Visible-Near-Infrared Spectroscopy for Enhanced Productivity inPlants (Basel, Switzerland) · 2024Article
- Influence and progress of tea pigment research: A comprehensive analysis of application of bibliometrics.Heliyon · 2024Article
- Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll.Plants (Basel, Switzerland) · 2024Article
- Uptake and bioaccumulation of iron oxide nanoparticles (FeEnvironmental science and pollution research international · 2024Article
- Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data.Environmental monitoring and assessment · 2024Article
- Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra.Forestry research · 2024Article
- Hyperspectral indices data fusion-based machine learning enhanced by MRMR algorithm for estimating maize chlorophyll content.Frontiers in plant science · 2024Article
- Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology.Foods (Basel, Switzerland) · 2023Review
- Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging.International journal of molecular sciences · 2023Article
- Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton (Plants (Basel, Switzerland) · 2023Article
- Chlorophyll Detection by Localized Surface Plasmon Resonance Using Functionalized Carbon Quantum Dots Triangle Ag Nanoparticles.Nanomaterials (Basel, Switzerland) · 2022Article
- Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance.Plants (Basel, Switzerland) · 2022Article
- Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves.Plant phenomics (Washington, D.C.) · 2022Article
- Hyperspectral Characteristics of an Individual Leaf of Wheat Grown under Nitrogen Gradient.Plants (Basel, Switzerland) · 2021Article
- Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms-random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)-in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 μg cm
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.