Discrimination of tea plant variety using in-situ multispectral imaging system and multi-feature analysis

Cao, Qiong; Yang, Guijun; Wang, Fan; Chen, Longyue; Xu, Baoxi; Zhao, Chunjiang; Duan, Dandan; Jiang, Ping; Xu, Ze; Yang, Haibin

doi:10.1016/j.compag.2022.107360

Cited by 14 publications

(12 citation statements)

References 54 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

“…Among these, σ, AL, NDALI, s, b, r, G, MTCI, and L contributed the most (VIP >1.0). Compared to Cao ( Cao et al., 2022b ), it is evident that σ plays a crucial role in identifying tea plant varieties, including oolong tea cultivars. Additionally, color information plays a more important role in the classification of oolong tea cultivars than other tea plant cultivars.…”

Section: Discussionmentioning

confidence: 99%

“…For a comprehensive understanding of the multispectral image processing procedures, please refer to Cao et al. ( Cao et al., 2022b ).…”

Section: Methodsmentioning

confidence: 99%

“…Previous research has suggested that the SVM algorithm exhibits certain benefits in categorizing different types of tea plants, achieving a relatively high level of precision ( Cao et al., 2022b ). The SVM algorithm was utilized as the primary classification technique for identifying oolong tea cultivars in this study.…”

Section: Methodsmentioning

confidence: 99%

“…By utilizing MSI technology, Cao et al. ( Cao et al., 2022b ) successfully developed a model for distinguishing 16 different types of tea cultivars, achieving highly accurate classification results. Th e use of multispectral imaging in conjunction with machine learning algorithms in these applications has established a foundation of knowledge and expertise for utilizing MSI technology to accurately identify tea cultivars.…”

Section: Introductionmentioning

confidence: 99%

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

Cao,

Zhao,

Bai

et al. 2023

Front. Plant Sci.

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Recognizing and identifying tea plant (Camellia sinensis) cultivar plays a significant role in tea planting and germplasm resource management, particularly for oolong tea. There is a wide range of high-quality oolong tea with diverse varieties of tea plants that are suitable for oolong tea production. The conventional method for identifying and confirming tea cultivars involves visual assessment. Machine learning and computer vision-based automatic classification methods offer efficient and non-invasive alternatives for rapid categorization. Despite advancements in technology, the identification and classification of tea cultivars still pose a complex challenge. This paper utilized machine learning approaches for classifying 18 oolong tea cultivars based on 27 multispectral characteristics. Then the SVM classification model was executed using three optimization algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The results revealed that the SVM model optimized by GWO achieved the best performance, with an average discrimination rate of 99.91%, 93.30% and 92.63% for the training set, test set and validation set, respectively. In addition, based on the multispectral information (h, s, r, b, L, Asm, Var, Hom, Dis, σ, S, G, RVI, DVI, VOG), the germination period of oolong tea cultivars can be completely evaluated by Fisher discriminant analysis. The study indicated that the practical protection of tea plants through automated and precise classification of oolong tea cultivars and germination periods is feasible by utilizing multispectral imaging system.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…For a comprehensive understanding of the multispectral image processing procedures, please refer to Cao et al. ( Cao et al., 2022b ).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%