Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Abstract

The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.

Oolong tea cultivars categorization and germination period classification based on multispectral information

“…The multispectral imaging system has shown to be a valid method for qualitatively and quantitatively monitoring tea quality ( Chen and Yan, 2020 ; Chen et al., 2021 ), and can assist in identifying tea plant varieties using the SVM method ( Cao et al., 2022b ). Initially, the SVM algorithm was applied to classify oolong tea cultivars, and achieved average accuracies of 99.79%, 91.31% and 90.62% for the training, test, and validation sets.…”

“…Typical spectral images obtained through multispectral imaging can also offer plentiful information regarding the object of detection. Multispectral imaging (MSI) technology has been demonstrated in numerous studies to allow for non-invasive and unbiased identification of plant phenotyping ( Chen et al., 2021 ), including but not limited to assessing fruit quality ( Liu et al., 2015 ) and distinguishing between different crop varieties ( Liu et al., 2016 ). By utilizing MSI technology, Cao et al.…”

Identifying and Counting Tobacco Plants in Fragmented Terrains Based on Unmanned Aerial Vehicle Images in Beipanjiang, China

“…where BF increases with the increase in the number of misclassified tobacco plants; DP is the percentage of correctly classified tobacco plants; QP represents the quality of the extraction results of tobacco plants; and the higher the QP value, the better the extraction results. On the whole, the BP value is negatively correlated with the DP and QP values, the smaller the BP value, the better the DP and QP values, indicating the better the extraction effect [46][47][48].…”

Hyperspectral image construction in different spectral bands of tea leafs for identifying the tea type using O-ConvNet-RF model

“…In hyper spectral imaging (HSI) data, the presence of noise can adversely affect machine learning applications, as highlighted in this research. Therefore, it is crucial to preprocess the HSI data using noise reduction techniques to improve the efficiency of tea class prediction, as depicted in (2) [22]. In the entire hyperspectral image acquisition system, the equipment used to capture the images can introduce noise reflectance, altering the distribution of emitted light reflectance across the surface of the material in the wavelength range covered by the spectral camera.…”