Big Data Analysis Applied in Agricultural Planting Layout Optimization

Abstract

Abstract. Mile is a region in Yunnan Province, China. The planting-related industry is its pillar industry. Its agricultural population accounts for 59.3% of the total population. Temporal fluctuations of crop price and yield have a significant influence on farmers’ revenue. Farmers’ selection of crop species, crop planting strategy, and agricultural planting layout according to the market price is important in securing their revenue. In this study, we used a web crawler program to obtain a large amount of data on agricultural product prices from the Internet. Then, the price fluctuation trend of the main economic crops was analyzed by using the K-means clustering method. The net investment yield and the Sharpe ratio were used to compare the economic benefits and investment risks of 10 crops and five cultivation strategies in Mile. Furthermore, a comprehensive comparative advantage index, which integrates the net investment yield, Sharpe ratio, scale advantage index, productivity advantage index, and ecological suitability advantage index, was adopted to comprehensively measure the advantages of crop cultivation. Finally, we propose a spatial-temporal big data analysis model based on the cuckoo search algorithm to optimize the spatial layout of the main crops in Mile in 2017. Based on the comparative analysis of the remote sensing monitoring results and the spatial optimization layout results of the main crops in 2017, several suggestions were given. The results based on agricultural big data analysis, such as crop selection cluster analysis, economic benefit analysis, and crop planting layout optimization, can give suggests to farmers plant suitable crops on right lands, in right time. Thus, it can help farmers stabilize their revenue and minimize the risk by choosing the right crops and planting strategy in accordance with the local conditions. Keywords: Agriculture investment risk, Agricultural layout optimization, Cuckoo search algorithm, K-means clustering, Relative advantage analysis, Spatial-temporal big data analysis.