Fresh Tea Shoot Maturity Estimation via Multispectral Imaging and Deep Label Distribution Learning

Chen, Bin; Yan, Jili

doi:10.1587/transinf.2020edl8038

Cited by 7 publications

(3 citation statements)

References 13 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%

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%

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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“…Many CNN-based improvement strategies [33][34][35][36] for object detection are focused on detection network structure or feature fusion. ere are also a few improvement works on output result, such as label distribution learning [37][38][39], but the improvement strategies on input data for FTSD are not published. Meanwhile, motivated by the multispectral image processing, we find that more input information can lead to a better detection result.…”

Section: Related Workmentioning

confidence: 99%

Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD

Chen

Yan

2021

Journal of Control Science and Engineering

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The accuracy of Fresh Tea Sprouts Detection (FTSD) is not high enough, which has become a big bottleneck in the field of vision-based automatic tea picking technology. In order to improve the detection performance, we rethink the process of FTSD. Meanwhile, motivated by the multispectral image processing, we find that more input information can lead to a better detection result. With this in mind, a novel Fresh Tea Sprouts Detection method via Image Enhancement and Fusion Single-Shot Detector (FTSD-IEFSSD) is proposed in this paper. Firstly, we obtain an enhanced image via RGB-channel-transform-based image enhancement algorithm, which uses the original fresh tea sprouts color image as the input. The enhanced image can provide more input information, where the contrast in the fresh tea sprouts area is increased and the background area is decreased. Then, the enhanced image and color image is used in the detection subnetwork with the backbone of ResNet50 separately. We also use the multilayer semantic fusion and scores fusion to further improve the detection accuracy. The strategy of tea sprouts shape-based default boxes is also included during the training. The experimental results show that the proposed method has a better performance on FTSD than the state-of-the-art methods.

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“…In addition, Parvathi and Tamil Selvi [9]. studied the maturity classification model of coconut, Huanghua pears [10], fresh tea shoot [11], and other agriculture products by using the method of deep learning and obtained good accuracy.…”

Section: Introductionmentioning

confidence: 99%

Muskmelon Maturity Stage Classification Model Based on CNN

Zhao

Lawal

et al. 2021

Journal of Robotics

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How to quickly and accurately judge the maturity of muskmelon is very important to consumers and muskmelon sorting staff. This paper presents a novel approach to solve the difficulty of muskmelon maturity stage classification in greenhouse and other complex environments. The color characteristics of muskmelon were used as the main feature of maturity discrimination. A modified 29-layer ResNet was applied with the proposed two-way data augmentation methods for the maturity stages of muskmelon classification using indoor and outdoor datasets to create a robust classification model that can generalize better. The results showed that code data augmentation which is the first way caused more performance degradation than input image augmentation—the second way. This established the effectiveness of the code data augmentation compared to image augmentation. Nevertheless, the two-way data augmentations including the combination of outdoor and indoor datasets to create a classification model revealed an excellent performance of F1 score ∼99%, and hence the model is applicable to computer-based platform for quick muskmelon stages of maturity classification.

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Fresh Tea Shoot Maturity Estimation via Multispectral Imaging and Deep Label Distribution Learning

Cited by 7 publications

References 13 publications

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

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

Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD

Muskmelon Maturity Stage Classification Model Based on CNN

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