Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data

He, Li; Wang, Yu; Fan, Kai; Mao, Yilin; Shen, Yaozong; Ding, Zhaotang

doi:10.3389/fpls.2022.898962

Cited by 13 publications

(5 citation statements)

References 42 publications

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“…Although the electronic nose, electronic tongue, and other rapid detection technologies have been widely used to detect the fermentation process of black tea, the design of these instruments is complex, so the measurement results are vulnerable to environmental changes [ 9 , 10 ]. In recent years, spectral analysis technology has been widely used in the determination of biochemical components and the quality analysis of tea leaves [ 11 ]. In 2013, Ren et al used near-infrared spectroscopy combined with a PLS algorithm to determine the main chemical components in black tea, such as CAF, TPs, and FAAs [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Mao

Wang

et al. 2022

Foods

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The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.

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Section: Introductionmentioning

confidence: 99%

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Mao

Wang

et al. 2022

Foods

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“…The network structures of PLS, SVM, RF, BP, CNN and LSTM were determined with reference to our two recent studies ( Li et al., 2022a ; Li et al., 2022b ). Because the network structure applied in previous studies have made better progress in the monitoring agronomic traits of tea plants, and the established models have a strong generalization ability and are applicable to tea plants.…”

Section: Methodsmentioning

confidence: 99%

“…In order to reduce dimensionality and remove redundant spectra, band selection methods such as successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) have been utilized by many researchers ( Li et al., 2016b ; Liu et al., 2019b ). Currently, various machine learning methods have been used to build models based on spectral images ( Li et al., 2022b ). For example, Chen et al ( Chen et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Low temperature response index for monitoring freezing injury of tea plant

Mao

He²,

Wang³

et al. 2023

Front. Plant Sci.

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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 (R2 = 0.890, RMSEP=0.325, RPD=2.904); (2) the feature bands screened by UVE algorithm were more abundant, and the later modeling effect was better than CARS and SPA algorithm; (3) comparing the effects of the six modeling algorithms, the overall modeling effect of the CNN model was better than other models. It can be concluded that out of all the combined models in this paper, the LTRI-UVE-CNN was a promising model for predicting the degree of low temperature stress in tea plants.

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“…[30] Precision farming China Tea plantations can be effectively managed by using UAV multi-source remote sensing. [31] Crop yield prediction India Wheat grain yield can be estimated significantly with satellite remote sensing algorithms and simulation models. [32] Determination of soil moisture China…”

Section: Herbicides Monitoring Germanymentioning

confidence: 99%

The Application of Remote Sensing Technologies for Enhancing Agroecosystems Performance

Omidi

Mehdizadeh

Al-Taey

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Human activity has dramatically altered the environment around the world. Most of these modifications occurred in natural habitats due to human activities. On the other hand, the variations in climatic conditions required to sustain appropriately performing ecosystems are becoming increasingly challenging for managers and policymakers. Often, pollution associated with human activities is diffuse, occurs over a wide geographic area, and accumulates over time. Assessing, monitoring, and eventually connecting relevant environmental variables with landscape development is crucial to overcoming these problems. Remote monitoring and evaluation of agroecosystem changes, using sensors, global positioning systems, and satellite-based platforms, provides for constant monitoring and assessment of field environment dynamics on a large scale. This study reviewed critical advances in remote sensing for the sustainable development of the agroecosystem.

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Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data

Cited by 13 publications

References 42 publications

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Low temperature response index for monitoring freezing injury of tea plant

The Application of Remote Sensing Technologies for Enhancing Agroecosystems Performance

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