Classification and Recognition of Crops Based on GIS Image

Abstract

Today, due to the rapid development of science and technology, new science and technology constantly change the status of agriculture, precision agriculture is developing rapidly. Precision agriculture is the combination of three new technologies: Global Positioning System (GPS), Remote Sensing Technology (RS) and Geographic Information System (GIS). In the 3S technology, GIS technology is the basis of realizing the other two technologies. It is a platform to achieve precision agriculture, supporting the input, analysis and output of agricultural resources data and other functions of precision agriculture. Classification and recognition of crop types is one of the most basic applications of precision agriculture. It can extract information from remote sensing images, accurately identify crop types and determine crop growth. In this paper, 7200 remote sensing image samples are divided into two groups: learning group and testing group. Combined with GIS image and RS technology, the characteristics of these four crops were summarized and counted through feature image extraction and feature data analysis of 5200 study group samples in GIS system. 2000 samples in the test group were used to calculate the recognition rate. Through experiments, we can know the recognition rate of peanut, spring maize, sweet potato and summer maize. The highest recognition rate is 98.60%, the lowest recognition rate is 90.20%, and the total recognition rate is 94.10%. It shows that in this experiment, the crop recognition rate of remote sensing image is very high in the application of classification and recognition based on GIS crop images. During the period when the spectral characteristics are very obvious, the recognition rate of crops in remote sensing images is higher, and the total recognition rate is increased by 3.98%. The NDVI values of crops in remote sensing images were analyzed and calculated, and the NDVI values of four crops in each growing period were obtained. By studying the NDVI value, we can determine the types of four crops and their growth period, and the accuracy of determining the types of crops is higher than that of determining the growth period of crops. Through the classification and identification of crop species and crop growth period, two experiments verify that the combination of GIS image and remote sensing image can improve the recognition accuracy of crop classification and identification, and the use of geospatial data in GIS can identify the differences between crops. By combining GIS images with remote sensing images, the development of precision agriculture can be promoted, and the reference for agricultural production and agricultural structure adjustment in designated areas can be provided.