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Machine learning and analytic hierarchy process integration for selecting a sustainable tractor.

Hassan A A Sayed1,2, Mahmoud A Abdelhamid3, Tarek Kh Abdelkader4,5

  • 1School of Energy and Environment Science, Yunnan Provincial Rural Energy Engineering Key Laboratory, Yunnan Normal University, Chenggong University Town, No. 768 Juxian Road, Kunming, 650500, P.R. China. hassan2712@azhar.edu.eg.

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Summary

This study simplifies tractor selection for small farms by combining the Analytic Hierarchy Process (AHP) and machine learning (ML). The optimal tractor balances price, power, and maintenance costs for sustainable and efficient farming.

Keywords:
AHPAgricultural mechanizationMachine learning in agricultureSmall farmsTractor selection

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Area of Science:

  • Agricultural Engineering
  • Decision Science
  • Sustainable Agriculture

Background:

  • Tractor selection for small farms involves complex technical, environmental, and economic factors.
  • Existing methods often lack efficiency and a focus on sustainability.

Purpose of the Study:

  • To simplify tractor selection for small-scale farms in the Egyptian Delta.
  • To identify key criteria aligned with sustainable development goals.
  • To integrate Analytic Hierarchy Process (AHP) and machine learning (ML) for optimized decision-making.

Main Methods:

  • Utilized Hierarchical Agglomerative Clustering with Euclidean distance to reduce nine criteria to three: price, power, and maintenance costs.
  • Applied AHP to weigh and prioritize these reduced criteria.
  • Evaluated four tractors (55-95 HP) based on expert input.

Main Results:

  • Price, power, and maintenance costs were identified as the most critical criteria with weights 0.142, 0.334, and 0.525, respectively.
  • Tractor T2 emerged as the optimal choice with a priority score of 0.326 (33.4%).
  • Tractor T1 (28.7%) and T3 (21%) were less optimal compared to T2.

Conclusions:

  • The integrated AHP and ML approach effectively simplifies tractor selection for small farms.
  • This method ensures the chosen tractor is sustainable, cost-efficient, and operationally effective.
  • The study provides a practical framework for farmers to make informed decisions.