Research on Optimal Crop Planting Plan Based on Big Bata Analysis and Genetic Algorithm

This study investigates the development of crop planting in rural North China through the use of big data analysis to pre-process and analyze historical as well as real-time data thereby constructing two mixed-integer programs based on planting decision models. In this model, if the level of farming exceeds the expected sales volume then it will not be sold or it will be sold at a lower price than its original price, while the aim is to maximize the profits from total sales which are constrained by actual production, crop rotation and plot planting rules. The second model, on the other hand is based on the assumption that surplus crops can be sold at low prices and has similar objective functions and constraints as the first model but with consideration for discounted sales. The above models were solved using genetic algorithms in our study, which simulated natural selection to deal with decision variables and nonlinear constraints under difficult conditions. For these two scenarios the total profits of 74,446,266.25 yuan and 135,819,338.75 yuan were yielded by the models after a hundred iterations respectively, thus confirming their validity and applicability as well as identifying scientific farming strategies for sustainable agricultural development.