Developing Situations of Tea Plucking Machine

Han, Yu; Xiao, Hongru; Qin, Guangming; Song, Zhiyu; Ding, Wenqin; Song, Malin

doi:10.4236/eng.2014.66031

Cited by 43 publications

(23 citation statements)

References 3 publications

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“…Hence, human operated machines for tealeaf plucking have been introduced with different levels of automation depending on the tealeaf value, tealeaf fragility and ground conditions (Wijeratne, 2012). Some of these machines need to be held out over tea bushes for many hours during long working days (Han et al , 2014). The musculoskeletal strain of doing so could be reduced by introduction of exoskeletons with gravity-balancing arms.…”

Section: Comparative Analysis – Exoskeletons For Different Manufacturing Phasesmentioning

confidence: 99%

Exoskeletons

Fox

Aranko²,

Heilala

et al. 2019

JMTM

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Purpose-Exoskeletons are mechanical structures that humans can wear to increase their strength and endurance. The purpose of this paper is to explain how exoskeletons can be used to improve performance across five phases of manufacturing. Design/methodology/approach-Multivocal literature review, encompassing scientific literature and the grey literature of online reports, etc., to inform comprehensive, comparative and critical analyses of the potential of exoskeletons to improve manufacturing performance. Findings-There are at least eight different types of exoskeletons that can be used to improve human strength and endurance in manual work during different phases of production. However, exoskeletons can have the unintended negative consequence of reducing human flexibility leading to new sources of musculoskeletal disorders (MSD) and accidents. Research limitations/implications-Findings are relevant to function allocation research concerned with manual production work. In particular, exoskeletons could exacerbate the traditional trade-off between human flexibility and robot consistency by making human workers less flexible. Practical implications-The introduction of exoskeletons requires careful health and safety planning if exoskeletons are to improve human strength and endurance without introducing new sources of MSD and accidents. Originality/value-The originality of this paper is that it provides detailed information about a new manufacturing technology: exoskeletons. The value of this paper is that it provides information that is comprehensive, comparative and critical about exoskeletons as a potential alternative to robotics across five phases of manufacturing.

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Section: Comparative Analysis – Exoskeletons For Different Manufacturing Phasesmentioning

confidence: 99%

Exoskeletons

Fox

Aranko²,

Heilala

et al. 2019

JMTM

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show abstract

“…Famous tea has high drinking value and economic benefits and is the pillar of the tea industry. At present, the picking of ordinary tea has been mechanized [3,4], whereas the picking of famous tea still relies on manual labor. With the increasing shortage of the tea picking labor force, the phenomenon of a "tea picker shortage" is becoming increasingly serious, thereby limiting the development of the tea industry [5,6].…”

Section: Introductionmentioning

confidence: 99%

Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea

Zhu

Tong

et al. 2021

Agriculture

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Accurately obtaining the posture and spatial position of tea buds through machine vision and other technologies is difficult due to the small size, different shapes, and complex growth environment of tea buds. Therefore, end effectors are prone to problems, such as picking omission and picking error. This study designs a picking end effector based on negative pressure guidance for famous tea. This end effector uses negative pressure to guide tea buds in a top-down manner, thereby correcting their posture and spatial position. Therefore, the designed end effector has deviation tolerance performance that can improve the picking success rate. The pre-experiment is designed, the tip of apical bud is referred to as the descent position, and the negative pressure range is determined to be 0.6 to 0.9 kPa. A deviation tolerance orthogonal experiment is designed. Experimental results show that various experimental factors are ranked in terms of the significance level of the effect on the average success rate, and the significance ranking is as follows: negative pressure (P) > pipe diameter (D) > descent speed (V). An evaluation method of deviation tolerance performance is presented, and the optimal experiment factor-level combination is determined as: P = 0.9 kPa, D = 34 mm, V = 20 mm/s. Within the deviation range of a 10 mm radius, the average success rate of the negative pressure guidance of the end effector is 97.36%. The designed end effector can be applied to the intelligent picking of famous tea. This study can provide a reference for the design of similar picking end effectors for famous tea.

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“…To minimize pickers' toil and grab the best picking time, some tea-picking machines have been developed (Slaughter et al 2008). However, existing tea harvesting machine works mainly in mechanical shearing mode (Han et al 2014), which simply cut the top of the tea tree with no selectivity. As a result, old leaves are "lumped" together with tender and broken leaves during harvesting; the process degrades the overall tea quality, a primary cause resulting in failing to meet the increasing demand of high-quality tea (Krahe et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Feature-set characterization for target detection based on artificial color contrast and principal component analysis with robotic tealeaf harvesting applications

Lü

Huang

Lee

2021

Int J Intell Robot Appl

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For the benefits of productivity, efficiency and sustainability, the adoption of machine vision with robotics in applications has become essential in agriculture industry; for example, outdoor tender-tealeaf harvesting, where tasks are highly repetitive and laborious. As machine vision must cope with changing daylight conditions, among the challenges is an efficient technique to accurately detect/locate randomly distributed targets in natural background that has closely similar color as the targets of interests. The success of developing a harvesting machine depends on robust, accurate, and efficient target-detection, which is the first step of the automation. Built upon the concept of an artificial color contrast (ACC) model developed for color-feature classification using principal component analysis (PCA), this paper presents an improved ACC/PCA method to overcome commonly encountered target-detection problems for outdoor agriculture applications where targets in a closely similar background must be identified/located in real time for subsequent robotic handling. Specifically, several methods have been developed to determine an optimal feature-set boundary for effective target detection, which require only a limited set of training data. The effectiveness of the methods is evaluated using experimentally obtained samples in terms of three practical measures (% detection error, % numerical noise and computation time) by comparing results with commonly used methods.

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Developing Situations of Tea Plucking Machine

Cited by 43 publications

References 3 publications

Exoskeletons

Exoskeletons

Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea

Feature-set characterization for target detection based on artificial color contrast and principal component analysis with robotic tealeaf harvesting applications

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