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A novel methodology for optimal land allocation for agricultural crops using Social Spider Algorithm.

N Thilagavathi1, T Amudha1

  • 1Department of Computer Applications, Bharathiar University, Coimbatore, Tamil Nadu, India.

Peerj
|October 3, 2019
PubMed
Summary

This study optimizes crop planning for maximizing agricultural income using the Social Spider Algorithm (SSA). The research provides insights for better land and water resource allocation in farming.

Keywords:
Bio-inspired AlgorithmsCrop planningMultiobjective optimizationOptimal Land AllocationSocial Spider Algorithm (SSA)

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Area of Science:

  • Agricultural Science
  • Optimization Techniques
  • Bio-inspired Computing

Background:

  • Limited arable land and the need for profitable crop allocation present significant challenges in modern agriculture.
  • Crop planning optimization, particularly land allocation, is a complex combinatorial optimization problem.
  • Bio-inspired algorithms offer effective solutions for complex optimization tasks.

Purpose of the Study:

  • To maximize net agricultural income through optimal land allocation strategies.
  • To apply a novel bio-inspired algorithm for solving the land optimization problem.
  • To provide data-driven insights for agricultural planning under resource constraints.

Main Methods:

  • The Social Spider Algorithm (SSA), a bio-inspired metaheuristic, was employed.
  • The SSA simulates the cooperative behavior of social spiders to find optimal solutions.
  • Data from Tamilnadu Agricultural University, Coimbatore, India, was used for a case study in the Coimbatore region.

Main Results:

  • The SSA computed optimal planting areas, crop productivity, and water requirements for various land holdings.
  • The algorithm demonstrated effectiveness in addressing land and water limitations.
  • Results indicated improved profit potential through optimized resource allocation.

Conclusions:

  • The Social Spider Algorithm is a viable tool for agricultural land optimization.
  • Optimal land allocation using SSA can enhance farm profitability.
  • The study offers practical guidance for agricultural planning, considering resource constraints.