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    Optimizing multi-machine path planning for crop precision seeding with Lovebird Algorithm

    Access Status
    In process
    Authors
    Utamima, A.
    Sulastri, M.J.
    Yuniarti, L.
    Ansaripoor, Amir Hossein
    Date
    2025
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Utamima, A. and Sulastri, M.J. and Yuniarti, L. and Ansaripoor, A.H. 2025. Optimizing multi-machine path planning for crop precision seeding with Lovebird Algorithm. Computers and Electronics in Agriculture. 235.
    Source Title
    Computers and Electronics in Agriculture
    DOI
    10.1016/j.compag.2025.110207
    ISSN
    0168-1699
    Faculty
    Faculty of Business and Law
    School
    School of Management and Marketing
    URI
    http://hdl.handle.net/20.500.11937/97447
    Collection
    • Curtin Research Publications
    Abstract

    This paper investigates path planning in agriculture, with a specific focus on the seeding process. It underscores the crucial role of path planning in enhancing the efficiency and productivity of agricultural machinery operations. The research is centered on minimizing the operational times for agricultural robots, encompassing sowing activities and auxiliary travel periods. The study compares the effectiveness of the Lovebird Algorithm against the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) in optimizing routes for precision seeding across various field layouts, addressing a range of geometric and operational challenges. The proposed Lovebird Algorithm demonstrates a runtime efficiency approximately three times faster than GA and one and a half times faster than ACO. Furthermore, it consistently reduces auxiliary travel distances by 14% compared to GA and 28% compared to ACO in the crop-seeding scenario. The findings align with the objectives of precision seeding by efficiently guiding machinery, thereby reducing travel-time and auxiliary travel distances. The proposed algorithm exhibits efficient computational performance, suggesting its suitability for time-sensitive agricultural operations that demand timely decision-making. Overall, the results have the potential to provide a tool that conserves resources and enhances efficiency in the agricultural sector, contributing to future advancements in precision agriculture technology.

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