ArticleScientific reports2020
Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 64 citations in OpenAlex.
- Computer Vision from Tea Cultivation to Quality Evaluation.Foods (Basel, Switzerland) · 2026Review
- Integrating Hyperspectral Data and Deep Learning for Non-Destructive Prediction of Tea Quality Parameters Across Different Physical States of Tea Leaves and Growth Periods.Plants (Basel, Switzerland) · 2026Article
- Advances in tea breeding in Japan: from traditional methods to genomic approaches.Breeding science · 2026Article
- Generalizability and transferability of machine learning models using hyperspectral reflectance data for maize traits.Scientific reports · 2026Article
- Coupling critical nitrogen dilution curve with hyperspectral feature optimization and machine learning for inversion of nitrogen nutrition index in greenhouse cucumber.Frontiers in plant science · 2026Article
- Prediction of leaf nitrogen in sugarcane (Heliyon · 2024Article
- Advances in the tea plants phenotyping using hyperspectral imaging technology.Frontiers in plant science · 2024Review
- Estimation of Biochemical Pigment Content in Poplar Leaves Using Proximal Multispectral Imaging and Regression Modeling Combined with Feature Selection.Sensors (Basel, Switzerland) · 2023Article
- Transcriptome Analysis of Maize Ear Leaves Treated with Long-Term Straw Return plus Nitrogen Fertilizer under the Wheat-Maize Rotation System.Plants (Basel, Switzerland) · 2023Article
- Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton (Plants (Basel, Switzerland) · 2023Article
- Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.Frontiers in plant science · 2023Article
- Spectroscopic analysis reveals that soil phosphorus availability and plant allocation strategies impact feedstock quality of nutrient-limited switchgrass.Communications biology · 2022Article
- Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves.Plant phenomics (Washington, D.C.) · 2022Article
- Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves.Scientific reports · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problematic. Previously, hyperspectral indices for chlorophyll (Chl) estimation, namely a green peak and red edge in the visible region, have been identified and used for N estimation because leaf N content closely related to Chl content in green leaves. Herein, datasets of N and Chl contents, and visible and near-infrared hyperspectral reflectance, derived from green leaves under various N nutrient conditions and albino yellow leaves were obtained. A regression model was then constructed using several machine learning algorithms and preprocessing techniques. Machine learning algorithms achieved high-performance models for N and Chl content, ensuring an accuracy threshold of 1.4 or 2.0 based on the ratio of performance to deviation values. Data-based sensitivity analysis through integration of the green and yellow leaves datasets identified clear differences in reflectance to estimate N and Chl contents, especially at 1325-1575 nm, suggesting an N content-specific region. These findings will enable the nondestructive estimation of leaf N content in tea plants and contribute advanced indices for nondestructive tracking of N status in crops.
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