Agriculture is not only the basis of national economic construction and development,but also the guarantee of social orderly operation.However,due to the huge losses caused by crop diseases and insect pests every year,it is of great significance for agricultural development to detect crop diseases and insect pests timely and accurately and take corresponding measures.In recent years,deep learning has made great progress in image recognition,in which Convolutional Neural Network has a good ability of image recognition,using this technology can accurately identify crop diseases and insect pests for timely prevention and control.First of all,this paper summarizes the traditional methods,machine learning methods and deep learning methods of crop pest identification,and analyzes and compares the advantages and disadvantages of the three methods.Secondly,The research of experts and scholars at home and abroad on the key technologies of crop disease and pest identification are described.This paper analyzes and summarizes the ways and scale of obtaining data sets,the functions of various preprocessing techniques of data sets,various methods of data set enhancement,the role of transfer learning and preprocessing of network models,the types and advantages and disadvantages of network models,and the characteristics,advantages and disadvantages of various optimization techniques of network models.Finally,the hot spots and difficulties of crop pest identification based on Convolutional Neural Network are pointed out,and its application prospect is prospected.
deep learningConvolutional Neural Networkimage recognitionkey technologypest identification
国家科技期刊平台
NSTL
万方数据
