ArticleFrontiers in plant science2021
Prediction of Drought-Induced Components and Evaluation of Drought Damage of Tea Plants Based on Hyperspectral Imaging.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Advances in tea breeding in Japan: from traditional methods to genomic approaches.Breeding science · 2026Article
- A lightweight hybrid transformer approach for hyperspectral imaging-based drought tolerance evaluation in tea plants.Plant methods · 2025Article
- Article
- Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery.Plant methods · 2024Article
- Article
- Advances in the tea plants phenotyping using hyperspectral imaging technology.Frontiers in plant science · 2024Review
- Application of Multi-Perspectives in Tea Breeding and the Main Directions.International journal of molecular sciences · 2023Review
- Estimating the frost damage index in lettuce using UAV-based RGB and multispectral images.Frontiers in plant science · 2023Article
- Low temperature response index for monitoring freezing injury of tea plant.Frontiers in plant science · 2023Article
- Drought stress identification of tomato plant using multi-features of hyperspectral imaging and subsample fusion.Frontiers in plant science · 2023Article
- Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging.Foods (Basel, Switzerland) · 2022Article
- Hyperspectral machine-learning model for screening tea germplasm resources with drought tolerance.Frontiers in plant science · 2022Article
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Authors and funding
8 authors.
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Abstract
Effective evaluation of physiological and biochemical indexes and drought degree of tea plant is an important technology to determine the drought resistance ability of tea plants. At present, the traditional detection method of tea drought stress is mainly based on physiological and biochemical detection, which is not only destructive to tea plants, but also time-consuming and laborious. In this study, through simulating drought treatment of tea plant, hyperspectral camera was used to obtain spectral data of tea leaves, and three machine learning models, namely, support vector machine (SVM), random forest (RF), and partial least-squares (PLS) regression, were used to model malondialdehyde (MDA), electrolyte leakage (EL), maximum efficiency of photosystem II (
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