Dataset Information

Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV.

ABSTRACT: It is generally feasible to classify different species of vegetation based on remotely sensed images, but identification of different sub-species or even cultivars is uncommon. Tea trees (Camellia sinensis L.) have been proven to show great differences in taste and quality between cultivars. We hypothesize that hyperspectral remote sensing would make it possibly to classify cultivars of plants and even to estimate their taste-related biochemical components. In this study, hyperspectral data of the canopies of tea trees were collected by hyperspectral camera mounted on an unmanned aerial vehicle (UAV). Tea cultivars were classified according to the spectral characteristics of the tea canopies. Furthermore, two major components influencing the taste of tea, tea polyphenols (TP) and amino acids (AA), were predicted. The results showed that the overall accuracy of tea cultivar classification achieved by support vector machine is higher than 95% with proper spectral pre-processing method. The best results to predict the TP and AA were achieved by partial least squares regression with standard normal variant normalized spectra, and the ratio of TP to AA-which is one proven index for tea taste-achieved the highest accuracy (RCV = 0.66, RMSECV = 13.27) followed by AA (RCV = 0.62, RMSECV = 1.16) and TP (RCV = 0.58, RMSECV = 10.01). The results indicated that classification of tea cultivars using the hyperspectral remote sensing from UAV was successful, and there is a potential to map the taste-related chemical components in tea plantations from UAV platform; however, further exploration is needed to increase the accuracy.

SUBMITTER: Tu Y

PROVIDER: S-EPMC5978401 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV.

Tu Yexin Y Bian Meng M Wan Yinkang Y Fei Teng T

PeerJ 20180528

It is generally feasible to classify different species of vegetation based on remotely sensed images, but identification of different sub-species or even cultivars is uncommon. Tea trees (<i>Camellia sinensis</i> L.) have been proven to show great differences in taste and quality between cultivars. We hypothesize that hyperspectral remote sensing would make it possibly to classify cultivars of plants and even to estimate their taste-related biochemical components. In this study, hyperspectral da ...[more]

PMID: 29868272

Similar Datasets

Project description:Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be used for N nutrition diagnosis and management. This work aims to explore the potential of multispectral imagery from unmanned aerial vehicle (UAV) for PNC estimation and improve the estimation accuracy with hyperspectral data collected in the field with a hyperspectral radiometer. In this study we combined selected vegetation indices (VIs) and texture information to estimate PNC in rice. The VIs were calculated from ground and aerial platforms and the texture information was obtained from UAV-based multispectral imagery. Two consecutive years (2015 & 2016) of experiments were conducted, involving different N rates, planting densities and rice cultivars. Both UAV flights and ground spectral measurements were taken along with destructive samplings at critical growth stages of rice (Oryza sativa L.). After UAV imagery preprocessing, both VIs and texture measurements were calculated. Then the optimal normalized difference texture index (NDTI) from UAV imagery was determined for separated stage groups and the entire season. Results demonstrated that aerial VIs performed well only for pre-heading stages (R2 = 0.52-0.70), and photochemical reflectance index and blue N index from ground (PRIg and BNIg) performed consistently well across all growth stages (R2 = 0.48-0.65 and 0.39-0.68). Most texture measurements were weakly related to PNC, but the optimal NDTIs could explain 61 and 51% variability of PNC for separated stage groups and entire season, respectively. Moreover, stepwise multiple linear regression (SMLR) models combining aerial VIs and NDTIs did not significantly improve the accuracy of PNC estimation, while models composed of BNIg and optimal NDTIs exhibited significant improvement for PNC estimation across all growth stages. Therefore, the integration of ground-based narrow band spectral indices with UAV-based textural information might be a promising technique in crop growth monitoring.

Project description:Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model's SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0-10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R 2 val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R 2 val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

Project description:BackgroundThe importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data.ResultsThree parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality.ConclusionsThe parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.

Project description:The tea plant (Camellia sinensis (L.) O. Kuntze) is one of the most economically important woody crops. Recently, many leaf color genotypes have been developed during tea plant breeding and have become valuable materials in the processing of green tea. Although the physiological characteristics of some leaf color mutants of tea plants have been partially revealed, little is known about the molecular mechanisms leading to the chlorina phenotype in tea plants.The yellow-leaf tea cultivar Zhonghuang 2 (ZH2) was selected during tea plant breeding. In comparison with Longjing 43 (LJ43), a widely planted green tea cultivar, ZH2 exhibited the chlorina phenotype and displayed significantly decreased chlorophyll contents. Transmission electron microscopy analysis revealed that the ultrastructure of the chloroplasts was disrupted, and the grana were poorly stacked in ZH2. Moreover, the contents of theanine and free amino acids were significantly higher, whereas the contents of carotenoids, catechins and anthocyanin were lower in ZH2 than in LJ43. Microarray analysis showed that the expression of 259 genes related to amino acid metabolism, photosynthesis and pigment metabolism was significantly altered in ZH2 shoots compared with those of LJ43 plants. Pathway analysis of 4,902 differentially expressed genes identified 24 pathways as being significantly regulated, including 'cysteine and methionine metabolism', 'glycine, serine and threonine metabolism', 'flavonoid biosynthesis', 'porphyrin and chlorophyll metabolism' and 'carotenoid biosynthesis'. Furthermore, a number of differentially expressed genes could be mapped to the 'theanine biosynthesis', 'chlorophyll biosynthesis' and 'flavonoid biosynthesis' pathways. Changes in the expression of genes involved in these pathways might be responsible for the different phenotype of ZH2.A novel chlorophyll-deficient chlorina tea plant cultivar was identified. Biochemical characteristics were analyzed and gene expression profiling was performed using a custom oligonucleotide-based microarray. This study provides further insights into the molecular mechanisms underlying the phenotype of the chlorina cultivar of Camellia sinensis.

Dataset Information

Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV.

Publications

Tea cultivar classification and biochemical parameter estimation from hyperspectral imagery obtained by UAV.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets