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CNN-MLP-Based Configurable Robotic Arm for Smart Agriculture

Author

Listed:
  • Mingxuan Li

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Faying Wu

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Fengbo Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Tianrui Zou

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Mingzhen Li

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Xinqing Xiao

    (College of Engineering, China Agricultural University, Beijing 100083, China)

Abstract

Amidst escalating global populations and dwindling arable lands, enhancing agricultural productivity and sustainability is imperative. Addressing the inefficiencies of traditional agriculture, which struggles to meet the demands of large-scale production, this paper introduces a highly configurable smart agricultural robotic arm system (CARA), engineered using convolutional neural networks and multilayer perceptron. CARA integrates a highly configurable robotic arm, an image acquisition module, and a deep processing center, embodying the convergence of advanced robotics and artificial intelligence to facilitate precise and efficient agricultural tasks including harvesting, pesticide application, and crop inspection. Rigorous experimental validations confirm that the system significantly enhances operational efficiency, adapts seamlessly to diverse agricultural contexts, and bolsters the precision and sustainability of farming practices. This study not only underscores the vital role of intelligent automation in modern agriculture but also sets a precedent for future agricultural innovations.

Suggested Citation

  • Mingxuan Li & Faying Wu & Fengbo Wang & Tianrui Zou & Mingzhen Li & Xinqing Xiao, 2024. "CNN-MLP-Based Configurable Robotic Arm for Smart Agriculture," Agriculture, MDPI, vol. 14(9), pages 1-16, September.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:9:p:1624-:d:1479436
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    References listed on IDEAS

    as
    1. Md. Mehedi Hasan & Touficur Rahman & A. F. M. Shahab Uddin & Syed Md. Galib & Mostafijur Rahman Akhond & Md. Jashim Uddin & Md. Alam Hossain, 2023. "Enhancing Rice Crop Management: Disease Classification Using Convolutional Neural Networks and Mobile Application Integration," Agriculture, MDPI, vol. 13(8), pages 1-17, August.
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