Identification of optimal crop plan using nature inspired metaheuristic algorithms

Abstract

The present study deals with the identification of optimal crop plan to improve the net benefits from the farming activities for the study area under consideration.Three nature inspired metaheuristic techniques namely Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are investigated to identify the most efficient crop plan to maximize the net farm benefits. Different resource constraints considered for the study are maximum available land area, ground water availability and cropped area for different crops. The obtained results are compared with the solutions from LINGO, software for Linear Programming (LP). The results reveal the net benefit per ha derived using DE, PSO, GA and LINGO are 73841.69, 73439.08, 70555.99 and 73841.65 Indian Rupees (INR) respectively for kharif crops and 20184.71, 20172.78, 19860.80 and 20184.70 `Indian Rupees (INR) respectively for rabi crops.

Multi-objective particle swarm optimization for regional crop planning

“…These crop planning models are based on the concept of linear programming and take a single objective function (Jain et al 2019). With the advent of open data policy, optimization software, and new programming paradigms like R and Python, it is now possible to develop multi-objective optimal crop plans regionally (Nath et al 2020).…”

“…The traditional approaches struggle with multi-objective search and optimization depending upon the domain. However, evolutionary algorithms are expected to outperform traditional approaches and other blind search tactics (Nath et al 2020). There is a lack of collective expertise in agriculture crop planning and evolutionary computing among researchers.…”