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Development and performance evaluation of variable width raised bed former with optimal parameters predicted by RBF

Chetankumar Prakash Sawant1, Bhaskar Bharat Gaikwad2, Ajit Pralhad Magar3

  • 1ICAR-Central Institute of Agricultural Engineering, Bhopal, 462 038, Madhya Pradesh, India. chetankumarsawant@gmail.com.

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Summary

This study developed a variable width raised bed former for efficient farming. Advanced modeling techniques optimized its performance, reducing energy use in field operations.

Keywords:
Draft measurementOptimizationRaised bed formerSoil binTillage equipment

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Area of Science:

  • Agricultural Engineering
  • Soil Science
  • Computational Modeling

Background:

  • Raised bed planting enhances soil health, water efficiency, and energy use.
  • Crop-specific needs require adaptable machinery like variable width bed formers.

Purpose of the Study:

  • To develop and evaluate a variable width raised bed former.
  • To optimize its operational parameters using advanced computational methods.

Main Methods:

  • Soil bin experiments analyzed draft force under varying soil moisture, working width, and forward speed.
  • Regression and Radial Basis Function (RBF) neural networks modeled specific draft.
  • RBF neural network-Particle Swarm Optimization (PSO) technique optimized input parameters.

Main Results:

  • Both regression and RBF models accurately predicted specific draft (R² > 0.98).
  • Optimal parameters yielded specific draft close to actual values in soil bin tests.
  • Field tests showed a ±7.2% variation between predicted and actual specific draft.

Conclusions:

  • Variable width raised bed formers are effective for diverse crop needs.
  • Neural network and PSO techniques successfully optimize tillage machinery parameters.
  • These methods reduce energy expenditure in agricultural field operations.