ArticleFrontiers in plant science2023
Low temperature response index for monitoring freezing injury of tea plant.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Deciphering "False Maturity" in Mountain Coffee: A Multimodal Hyperspectral Framework for Non-Destructive Sugar Content Assessment.Foods (Basel, Switzerland) · 2026Article
- Transcriptome Analysis Coupled with Metabolome Profiling at a Key Time Point Reveals the Molecular Mechanism of Cold Stress Response in Oil Palm (Plants (Basel, Switzerland) · 2026Article
- Comparative Transcriptomics and Metabolomics Analysis Revealed the Mechanism of Exogenous Salicylic Acid Improving the Cold Tolerance of Walnut.International journal of molecular sciences · 2026Article
- A lightweight hybrid transformer approach for hyperspectral imaging-based drought tolerance evaluation in tea plants.Plant methods · 2025Article
- Ploidy levels influence cold tolerance of Cyclocarya paliurus: insight into the roles of WRKY genes.BMC genomics · 2025Article
- Rapid Color Quality Evaluation of Needle-Shaped Green Tea Using Computer Vision System and Machine Learning Models.Foods (Basel, Switzerland) · 2024Article
- Advances in the tea plants phenotyping using hyperspectral imaging technology.Frontiers in plant science · 2024Review
- Effect of Shading on Physiological Attributes and Proteomic Analysis of Tea during Low Temperatures.Plants (Basel, Switzerland) · 2023Article
- Monitoring and Mapping Winter Wheat Spring Frost Damage with MODIS Data and Statistical Data.Plants (Basel, Switzerland) · 2023Article
- A deep learning model for rapid classification of tea coal disease.Plant methods · 2023Article
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Authors and funding
11 authors.
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Abstract
Freezing damage has been a common natural disaster for tea plantations. Quantitative detection of low temperature stress is significant for evaluating the degree of freezing injury to tea plants. Traditionally, the determination of physicochemical parameters of tea leaves and the investigation of freezing damage phenotype are the main approaches to detect the low temperature stress. However, these methods are time-consuming and laborious. In this study, different low temperature treatments were carried out on tea plants. The low temperature response index (LTRI) was established by measuring seven low temperature-induced components of tea leaves. The hyperspectral data of tea leaves was obtained by hyperspectral imaging and the feature bands were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE). The LTRI and seven indexes of tea plant were modeled by partial least squares (PLS), support vector machine (SVM), random forests (RF), back propagation (BP) machine learning methods and convolutional neural networks (CNN), long short-term memory (LSTM) deep learning methods. The results indicated that: (1) the best prediction model for the seven indicators was LTRI-UVE-CNN (R
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