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Novel Technical Parameters-Based Classification of Harvesters Using Principal Component Analysis and Q-Type Cluster Model

Author

Listed:
  • Kibiya Abubakar Yusuf

    (Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Edwin O. Amisi

    (Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Qishuo Ding

    (Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Xinxin Chen

    (College of Agricultural Engineering, Jiangsu University, Zhenjiang 210031, China)

  • Gaoming Xu

    (Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Abdulaziz Nuhu Jibril

    (Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Moussita G. Gedeon

    (Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Zakariya M. Abdulhamid

    (Software College, Northeastern University, Heping District, Shenyang 110004, China)

Abstract

The advancement of agricultural mechanization necessitates precise and standardized classification based on technical characteristics to enhance green, efficient, and high-quality development. The current lack of scientific and standardized definitions and classifications for various types of agricultural machinery has become a bottleneck, complicating the machine selection and affecting the compatibility of the machinery with optimized field operations. To address this complexity, we propose a comprehensive classification method that integrates principal component analysis (PCA), cluster analysis, and the qualitative analysis of the functional components for defining and scientifically classifying harvesters. The key functional and technical properties of harvesters were analyzed, and eight primary parameters (machine weight, cutting width, feed rate, rated power, overall machine length, width, height, and working efficiency) were selected, supplemented by nine key functional components (walking mechanism, cutting device, threshing device, separating device, cleaning device, grain collecting device, grain unloading device, cabin, and track size). In the first step, principal component analysis was performed to reduce the dimensionality of the parameters, yielding three principal components with contribution rates of 41.610%, 28.579%, and 15.134%, respectively. One primary parameter from each component was selected for further analysis. In the second stage, Q-type cluster analysis classified the harvesters based on the squared Euclidean distance between the operational parameters, resulting in three classes of harvesters. Finally, functional component analysis provided detailed insights, further refining the classification into four major categories: mini, small, medium, and large harvesters. The results of this work provide a scientific basis for the definition and classification of the harvester products available on the market. This method offers a robust framework for the rational selection and planning of agricultural machinery, promoting sustainable mechanization with a focus on technical parameters and functional attributes.

Suggested Citation

  • Kibiya Abubakar Yusuf & Edwin O. Amisi & Qishuo Ding & Xinxin Chen & Gaoming Xu & Abdulaziz Nuhu Jibril & Moussita G. Gedeon & Zakariya M. Abdulhamid, 2024. "Novel Technical Parameters-Based Classification of Harvesters Using Principal Component Analysis and Q-Type Cluster Model," Agriculture, MDPI, vol. 14(6), pages 1-16, June.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:6:p:941-:d:1416023
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    References listed on IDEAS

    as
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