Research on apple orchard classification and yield estimation model based on GF-1 and Sentinel-2

Abstract

China is a big apple planting country and attaches great importance to the development of apple industry in agricultural economy. There are many mountainous areas in Shaanxi Province, which has obvious geographical advantages and is one of the important areas for apple production in my country. A quick and effective forecast of the apple output in Shaanxi Province can not only strengthen the management of apple planting and production, improve the varieties of apple production, and improve the quality of apple production, but also provide technical support for regional agricultural departments to expand the apple market and improve the base construction. It is of great significance to promote the rapid development of my country's apple planting industry. In this study, Luochuan County, Yana's City, Shaanxi Province was used as the research area, using GF-1 and Sentinel-2 multispectral remote sensing images and their vegetation indices from 2013 to 2019, and using RF to extract orchards in the research area. Secondly, combining the classification results with rainfall, temperature, sunshine hours, air pressure, humidity, wind speed, drought indicators and remote sensing vegetation index, using RFR and SVR methods, establish a comprehensive production estimation model suitable for Luochuan County apples, and compare different types Model accuracy. The main conclusions are drawn through the research: Using RF classification method can effectively extract the luochuan orchard distribution and high precision, based on RFR and SVR method combined with meteorological factor, the drought index and remote sensing vegetation index to establish basic quite, crop yield estimation model precision machine learning regression algorithm for subsequent apple luochuan orchard management, and provide strong decision basis for the development of apple industry.