Investigation on the Bending Behavior of Tea Stalks Based on Non-Prismatic Beam with Virtual Internodes

Wu, Wenchao; Hu, Yongguang; Jiang, Zehui

doi:10.3390/agriculture12030370

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Considering the existence of current tea picking machines that damage tea stalks and tea buds, to be able to optimize the current tool structure. Wu et al (2022) investigated the bending of tea stalks under conditions of large perturbations. The tea stalk disturbance curve fitted by the experiment was helpful in finding the optimal picking area and guided the movement speed and cutting frequency of the end-effector during the working process.…”

Section: Tea Harvestmentioning

confidence: 99%

“…Due to the lack of publicly available datasets, most research scholars use their own collected data to complete the training and validation of the algorithm. Moreover, some studies (Hu et al, 2019a,b;Wu et al, 2022) utilized a small amount of image data and did not contain rich features. It led to a large variation in the results of some studies and a lack of specific benchmarks.…”

Section: Open Datasets In the Tea Industrymentioning

confidence: 99%

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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: Tea Harvestmentioning

confidence: 99%

Section: Open Datasets In the Tea Industrymentioning

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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Biomechanical Characterization of Bionic Mechanical Harvesting of Tea Buds

Luo

Cao

et al. 2022

Agriculture

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To date, mechanized picking of famous tea (bud, one bud one leaf) causes a lot of damage. Manual picking results in high-quality tea but the process is inefficient. Therefore, in order to improve the quality of mechanically harvested tea buds, the study of bionic picking is beneficial to reduce the damage rate of mechanical picking. In this paper, the manual flexible picking process is studied, and a bionic bladeless mechanical picking mechanics model is developed. The relationship between the mechanical properties and structural deformation of tea stalks is obtained by microstructural observation and mechanical experimental analysis and determination of the bud bionic picking mechanics flow by combined loading tests is carried out. The results show that the key factor for low damage in tea picking is the precise flexible force applied to different parts of the shoot tip during pinching, upward, and picking. The biological force of tea stalks is closely related to the stalk diameter and maturity of stalk tissue development. The larger the xylem of the tea stalk, the stronger its resistance to bending, stretching, and deformation. The stalks at the tender end of the tea are more resilient than the lower stalks and will not break under the action of large angle bending. Additionally, the stalks at the shoot tip have significantly lower pull-off force than the stalks at other places. By simulating the manual picking process, the mechanical picking mechanical parameters were determined to be a clamping pressure of 340 kPa, bending force of 0.134 N, and pull-off force of 5.1 N. These findings help the design of low-damage pickers for famous tea and provide a reference for low-damage bionic picking of tea.

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Performance Test of Artificial Defoliating Broccoli Conveyor Line and Analysis of Defoliating Broccoli Inflorescences

Cao,

Tang,

et al. 2024

Agronomy

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There is a close relationship between stem and leaf biological characteristics of mature broccoli plants and defoliation technology. Morphological parameters such as the spread degree and diameter of cauliflower stem and leaf and the connection performance of cauliflower stem and leaf were studied. These experiments are helpful to the research of defoliation devices and methods for large-scale manual transportation lines. In this paper, according to the damage forms that may be suffered in the separation process of mature broccoli stems and leaves, “sunny” broccoli varieties were selected. Firstly, the mechanical properties of mature broccoli stems were obtained through different loading methods such as stretching, compression, shearing and bending. Secondly, the stress relaxation characteristics of broccoli were analyzed by static compression of broccoli flower balls. Finally, the separation method of broccoli stems and leaves was simulated by ANSYS. The simulation results showed that shear fracture was more suitable for stem and leaf separation of broccoli than tensile fracture. Then, aiming at the separation of stems and leaves of broccoli, an artificial broccoli assembly line was proposed to ensure the efficiency of leaf removal through man–machine cooperation. The dynamic characteristics of the belt of broccoli leaf removal line were studied to ensure the efficient and stable operation of the conveyor system of broccoli artificial leaf removal line.

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Investigation on the Bending Behavior of Tea Stalks Based on Non-Prismatic Beam with Virtual Internodes

Cited by 3 publications

References 17 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

Biomechanical Characterization of Bionic Mechanical Harvesting of Tea Buds

Performance Test of Artificial Defoliating Broccoli Conveyor Line and Analysis of Defoliating Broccoli Inflorescences

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