Optimal Crop Selection Using Multiobjective Evolutionary Algorithms

Abstract

Farm managers have to deal with many conflicting objectives when planning which crop to cultivate. Soil characteristics are extremely important when determining yield potential. Fertilization and liming are commonly used to adapt soils to the nutritional requirements of the crops to be cultivated. Planting the crop that will best fit the soil characteristics is an interesting alternative to minimize the need for soil treatment, reducing costs and potential environmental damages. In addition, farmers usually look for investments that offer the greatest potential earnings with the least possible risks. According to the objectives to be considered the crop selection problem may be difficult to solve using traditional tools. Therefore, this work proposes an approach based on Multiobjective Evolutionary Algorithms to help in the selection of an appropriate cultivation plan considering five crop alternatives and five objectives simultaneously.

A Meta-Heuristics Based Decision Support System for Optimal Crop Planning

“…The linear programming model gained its popularity because of its simplicity and its ability of solving selection problems with various objectives (Leroy and Jacquin, 1991). Some problems associated with the use of this method take account of the difficulties in formulating the problem's objectives, constraints and deducing its results (Buick et al, 1992). The original linear programming framework has been extended in several application areas to reduce its limitations (Adeyemo and Otieno, 2009).…”

“…Lately, evolutionary optimization algorithms have been employed in addressing multi-objective crop planning problems at farm level (Brunelli and von Lücken, 2009), at a nationwide level (Sarker and Ray, 2009) and provincial scale (DeVoil et al, 2006). The advantage of using evolutionary optimization algorithms is to get a set of solutions obtained from a set of Pareto optimal solution (Coello, 2009).…”

“…Certain authors (Stone et al, 1992;Nevo et al, 1994) have indicated that applying only deterministic and quantifiable approaches is not sufficient to realize suitable crop planning due to the type of facts needed; as such facts is frequently uncertain, qualitative and incomplete. Nevo et al (1994) incorporated an expert system technology with a traditional linear and mathematical programming technique to provide a solution to these setbacks.…”

“…The rules are predicated on expert's knowledge and are logically quantified using fuzzy logic techniques and uncertain processes using Bayesian theory. Stone et al (1992) used artificial intelligence to solve the crop planning problem, not using traditional optimization methods (Dury et al, 2012).…”

An Investigation of Generalized Differential Evolution Metaheuristic for Multiobjective Optimal Crop-Mix Planning Decision

“…One of such reasons is that evolutionary metaheuristics need little problem specific knowledge and can be applied to a broad range of problem types [ 7 ]. The evolutionary metaheuristics required the target objective function for a given problem to be optimized, but additional problem specific knowledge can be easily brought into metaheuristics to improve their performances [ 8 ]. In addition, metaheuristics require no derivative information; they are robust, flexible, and relatively simple to implement [ 9 ].…”