Related Study Based on Otsu Watershed Algorithm and New Squeeze-and-Excitation Networks for Segmentation and Level Classification of Tea Buds

Qi, Fang; Xie, Zuoqi; Tang, Zhe; Chen, Huarong

doi:10.1007/s11063-021-10501-1

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…The evaluation metrics currently are insufficiently adapted to the specific application. Some studies Qi et al, 2021; still used only metrics from the computer domain to evaluate the excellence of the methods, for example, the mIoU, mAP, and F1 scores. Although these indicators can reflect the strengths and weaknesses of the algorithm in terms of accuracy, the readability is poor and not representative.…”

Section: E Ective Evaluation Indicatorsmentioning

confidence: 99%

A review on the application of computer vision and machine learning in the tea industry

Wang

2023

Front. Sustain. Food Syst.

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Tea is rich in polyphenols, vitamins, and protein, which is good for health and tastes great. As a result, tea is very popular and has become the second most popular beverage in the world after water. For this reason, it is essential to improve the yield and quality of tea. In this paper, we review the application of computer vision and machine learning in the tea industry in the last decade, covering three crucial stages: cultivation, harvesting, and processing of tea. We found that many advanced artificial intelligence algorithms and sensor technologies have been used in tea, resulting in some vision-based tea harvesting equipment and disease detection methods. However, these applications focus on the identification of tea buds, the detection of several common diseases, and the classification of tea products. Clearly, the current applications have limitations and are insufficient for the intelligent and sustainable development of the tea field. The current fruitful developments in technologies related to UAVs, vision navigation, soft robotics, and sensors have the potential to provide new opportunities for vision-based tea harvesting machines, intelligent tea garden management, and multimodal-based tea processing monitoring. Therefore, research and development combining computer vision and machine learning is undoubtedly a future trend in the tea industry.

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Section: E Ective Evaluation Indicatorsmentioning

confidence: 99%

A review on the application of computer vision and machine learning in the tea industry

Wang

2023

Front. Sustain. Food Syst.

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“…The test sample is placed in the test device shown in Figure 1 with the help of a high-speed camera, we can arbitrarily obtain an image of a frame in the whole experiment, as shown in Figure 2. The obtained image was imported into Matlab program, and after binarization, the overall contour extraction of the whole coal sample is achieved by using the output result of the watershed algorithm (Xiong et al, 2020;Qi et al, 2021) (see Figure 4). We binarization each image just like Figure 5.…”

Section: Information Extraction Technology Of Adsorption Expansion Ba...mentioning

confidence: 99%

Study on key influencing factors of competitive adsorption of coalbed methane by carbon dioxide displacement

et al. 2022

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The extraction of coal bed methane (CBM) by injecting CO2 into deeply buried unmined coal seams in competition with CH4 adsorption to provide a clean fuel is known as enhanced coal bed methane recovery (ECBM) and has proven to be an effective technological strategy to address global warming. The study of the interaction of coal with CO2 and CH4 under multi-physical field conditions is particularly necessary. In this work, a series of experiments were conducted on a home-made test system to investigate the competing sorption patterns of high and medium ash coal samples subjected to variables such as gas pressure, temperature, nodulation and lateral limit constraints. The results show that there is a sorption isotherm relationship between coal samples and exposure time. The adsorption capacity sorption of CH4/CO2 varied considerably for different ash coal samples. As the CO2 pressure increased from 2.3 to 5.5 MPa, the strain on the coal samples increased from 0.082 to 0.4%. The deformation in the vertical laminae direction is always greater than that in the parallel laminae direction. A correlation coefficient K exists between 1 and 2, and there is an internal expansion pattern in the adsorption deformation of coal. This paper can contribute to the improvement of ECBM efficiency.

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“…This method achieves effective localization of tea buds. Qi et al (2021) combined the "Maximum Between-Class Variance Method" (Otsu) with the traditional watershed algorithm to determine the threshold for image segmentation, thereby improving the accuracy of segmentation. They also improved the SE module to enhance the performance of deep learning networks and achieved outstanding accuracy on the tea bud dataset.…”

Section: Introductionmentioning

confidence: 99%

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

Zhang,

Sun,

Xie

et al. 2023

Front. Plant Sci.

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IntroductionThe identification and localization of tea picking points is a prerequisite for achieving automatic picking of famous tea. However, due to the similarity in color between tea buds and young leaves and old leaves, it is difficult for the human eye to accurately identify them.MethodsTo address the problem of segmentation, detection, and localization of tea picking points in the complex environment of mechanical picking of famous tea, this paper proposes a new model called the MDY7-3PTB model, which combines the high-precision segmentation capability of DeepLabv3+ and the rapid detection capability of YOLOv7. This model achieves the process of segmentation first, followed by detection and finally localization of tea buds, resulting in accurate identification of the tea bud picking point. This model replaced the DeepLabv3+ feature extraction network with the more lightweight MobileNetV2 network to improve the model computation speed. In addition, multiple attention mechanisms (CBAM) were fused into the feature extraction and ASPP modules to further optimize model performance. Moreover, to address the problem of class imbalance in the dataset, the Focal Loss function was used to correct data imbalance and improve segmentation, detection, and positioning accuracy.Results and discussionThe MDY7-3PTB model achieved a mean intersection over union (mIoU) of 86.61%, a mean pixel accuracy (mPA) of 93.01%, and a mean recall (mRecall) of 91.78% on the tea bud segmentation dataset, which performed better than usual segmentation models such as PSPNet, Unet, and DeeplabV3+. In terms of tea bud picking point recognition and positioning, the model achieved a mean average precision (mAP) of 93.52%, a weighted average of precision and recall (F1 score) of 93.17%, a precision of 97.27%, and a recall of 89.41%. This model showed significant improvements in all aspects compared to existing mainstream YOLO series detection models, with strong versatility and robustness. This method eliminates the influence of the background and directly detects the tea bud picking points with almost no missed detections, providing accurate two-dimensional coordinates for the tea bud picking points, with a positioning precision of 96.41%. This provides a strong theoretical basis for future tea bud picking.

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Related Study Based on Otsu Watershed Algorithm and New Squeeze-and-Excitation Networks for Segmentation and Level Classification of Tea Buds

Cited by 10 publications

References 27 publications

A review on the application of computer vision and machine learning in the tea industry

A review on the application of computer vision and machine learning in the tea industry

Study on key influencing factors of competitive adsorption of coalbed methane by carbon dioxide displacement

A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model

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