Study On The Agriculture Farming Plan Optimizing With Genetic Algorithm

Modern agriculture required high production, high efficiency, high quality and low cost. Precision fanning is just following this trend. It integrated modern informatics, agronomy, agricultural engineering and technology into agricultural production to gain a high farming performance. Yearly farming planning optimizing was a important problem in precision fanning.The problems of farming planning optimizing were mainly discussed in this thesis. As a typical NP hard problem, the traditional optimizing methods can hardly get a satisfied solution.Genetic algorithm is one of the random searching algorithms with many excellent characteristics. Based on the analysis to genetic algorithm, this study proposed a farming planning optimizing model, and developed a farming planning optimizing system.First of all, the method for farming planning optimizing, based on genetic algorithm, was analyzed theoretically in this thesis. A coding scheme with 0-1 for the describing of target matrix and an integer type chromosome coding scheme was proposed. The fitness functions under 3 different policy conditions were described respectively in this thesis. Secondly, the farming planning CIMS system was analyzed and designed. The farming planning optimizing system was developed by the developing tool of Visual Basic 6.0.Based on this system, data experiments were conducted for farming planning optimizing. Experimental result indicated that the farming planning optimizing model with genetic algorithm has a very good performance in crop planning optimizing.Finally, the problem of parameter selection in farming planning optimizing based on genetic algorithm was studied through father data experiment. Some conclusions were gained from the result of data experiment. An excellent individual keeping policy could improve the efficiency in chromosome evolution. Lower mutation rate will cause premature convergence and bring the colony into a local optimal area, while a higher mutation rate will cause colony degeneration andreduce the efficiency in chromosome evolution. The cross rate has no significant influence in the genetic algorithm model proposed in this study.