An optimal cropping pattern lets us increase profit in agriculture with minimum human efforts. The objective of this study is to formulate, simulate and evaluate a genetic algorithm-driven model to maximize crop yields and expend minimum effort. This study develops a nonlinear mixed-integer programming model and employs a genetic algorithm to optimize the stipulated objectives. I conducted experiments to compare the performance of the cropping patterns optimized by different objective functions in terms of expected fitness functions.
- The profit made depends on cost of seeds and fertilizers and market value of the produce (both these quantities are wrt to a single plant).
- Human efforts required for growing these crops depends upon growth periods of the plants, soil and climate suitability for growing that plant.
- The total area for growing crops is fixed and can’t exceed A (A= 100)
- Crop Name
- Growth Period
- Cost Of Seed And Fertilisers
- Market Value Of Produce
- Human Effort
- Climate Suitabilty
- Soil Suitability