DESIGN AND EXPERIMENTAL STUDY OF HIGH-EFFICIENCY AND LOW-DAMAGE BIONIC PICKING DEVICE FOR TOBACCO LEAVES

To address the issues of high damage rate and low harvesting efficiency during the tobacco leaf picking process, this study analyzed the separation mechanics of tobacco stems and leaves. A low-damage bionic picking device was designed by imitating the manual method of harvesting tobacco leaves. Based on theoretical analysis of the device and its key components, the structure and parameters of the complete system were determined. The picking process was simulated using ADAMS software, focusing on the contact force between the rigid and flexible components at various picking rod speeds. This analysis yielded the optimal combination of structural and operational parameters. Field experiments were subsequently conducted, and a response surface mathematical model was established using Design-Expert software to evaluate the relationship between key factors and performance indicators. The optimal parameter combination was found to be: a picking rod speed of 0.8 m/s, a device inclination angle of 30°, a picking rod spacing of 90 mm, and a forward speed of 0.69 m/s. Under these conditions, the tobacco leaf damage rate was minimized, meeting the requirements for low-damage harvesting. Further experimental validation showed consistency with simulation results, confirming the model's reliability and demonstrating the practical feasibility of the device for tobacco field operations. This provides a valuable reference for the development of low-damage tobacco leaf harvesting equipment.

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Publication Details

Journal
INMATEH Agricultural Engineering
Published
2025-04-30
DOI
https://doi.org/10.35633/inmateh-75-84
Citations
1
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.91

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article

DESIGN AND EXPERIMENTAL STUDY OF HIGH-EFFICIENCY AND LOW-DAMAGE BIONIC PICKING DEVICE FOR TOBACCO LEAVES

Baoshan Wang, Chenhui Zhu, Bo Luo, Bingjie Chen et al.
1 citations
INMATEH Agricultural Engineering
Neuroscience and Neural Engineering
0.91
article

DESIGN AND EXPERIMENTAL STUDY OF HIGH-EFFICIENCY AND LOW-DAMAGE BIONIC PICKING DEVICE FOR TOBACCO LEAVES

Baoshan Wang, Chenhui Zhu, Bo Luo, Bingjie Chen, Wanzhang Wang, Liquan Yang, Bingjie Liu
article en
1 citations

Abstract

To address the issues of high damage rate and low harvesting efficiency during the tobacco leaf picking process, this study analyzed the separation mechanics of tobacco stems and leaves. A low-damage bionic picking device was designed by imitating the manual method of harvesting tobacco leaves. Based on theoretical analysis of the device and its key components, the structure and parameters of the complete system were determined. The picking process was simulated using ADAMS software, focusing on the contact force between the rigid and flexible components at various picking rod speeds. This analysis yielded the optimal combination of structural and operational parameters. Field experiments were subsequently conducted, and a response surface mathematical model was established using Design-Expert software to evaluate the relationship between key factors and performance indicators. The optimal parameter combination was found to be: a picking rod speed of 0.8 m/s, a device inclination angle of 30°, a picking rod spacing of 90 mm, and a forward speed of 0.69 m/s. Under these conditions, the tobacco leaf damage rate was minimized, meeting the requirements for low-damage harvesting. Further experimental validation showed consistency with simulation results, confirming the model's reliability and demonstrating the practical feasibility of the device for tobacco field operations. This provides a valuable reference for the development of low-damage tobacco leaf harvesting equipment.

INMATEH Agricultural Engineering
Pingdingshan University (CN), Henan Agricultural University (CN), Henan University of Economic and Law (CN)
China National Tobacco Corporation, Henan Provincial Science and Technology Research Project
Openalex Percentile: Top 28%
Neuroscience and Neural Engineering
0.91
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