Study On Pineapple Planting Area Extraction Technology Based On Remote Sensing Data

Pineapple is a popular tropical fruit,as well as one of the main cash crops in China.It is mostly planted in coastal cities in south China.Xuwen County is one of China’s important pineapple planting and production export bases,with the annual output accounting for about 30% of the country.In recent years,due to the influence of comprehensive factors such as weather and market supply and demand relations,the planting area of pineapple in Xuwen county has slightly fluctuated,and the price of pineapple is unstable.Therefore,timely acquisition of pineapple planting area information is helpful to improve the economic benefits of pineapple industry in Xuwen County.With the rapid development of remote sensing technology,remote sensing data quantity is becoming more and more big,make the earth on which we can obtain abundant information resources,and remote sensing technology has a wide range of observation and revisit cycle ability to bring the advantages of shorter also increasingly prominent,has been widely used in the extraction and plant diseases and insect pests monitoring crop planting area,and gradually replace traditional statistical investigation way,but there is few studies on remote sensing monitoring of pineapple smell,so to carry out the pineapple planting area on the basis of the means of remote sensing technology research,has important practical significance.In this paper,Phantom 4 RTK aviation remote sensing data and Sentinel-2A satellite remote sensing data were used as data sources to carry out research on the extraction technology of pineapple planting areas in Xuwen County.First,the aerial remote sensing data of uav and the optimal band combination algorithm were used to establish the ground object interpretation logo of pineapple on the satellite remote sensing image,and then three traditional classification methods,namely K-nearest Neighbor(KNN),artificial neural network and Support Vector Machine(SVM),were used to extract the pineapple planting area.Finally,the idea of integrated learning is proposed to extract pineapple planting areas in Xuwen County.This paper uses Adaboost.M2 algorithm principle to train multiple basic classiators,and uses voting combination to obtain the final strong classifier.The conclusions of this paper are as follows:(1)Sentinel-2A remote sensing image has a large number of bands.The OIF index factor is used to analyze multiple bands of pineapple region of interest image,and the combination sequence of B11-B8-B5 bands with red edge bands is determined as the optimal visual identification band combination sequence of pineapple extraction.(2)Using Phantom 4 RTK aerial remote sensing data to select samples of Sentinel-2A satellite remote sensing images and establish interpretation markers are far better than those generated based on field record coordinate points in terms of accuracy and visual effect.(3)This paper proposes that the precision of pineapple planting area extraction using integrated learning method is better than other traditional machine learning methods.Currently in pineapple crop planting area of extraction,few studies on this article studied integrated learning method based on multi-source remote sensing data can also be used for subsequent application in pineapple planting area extraction provides reference experience,at the same time can also be grasped the pineapple growing conditions for the local agricultural departments to provide certain help.