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

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.