摘要
农业不仅是国民经济建设与发展的基础,也是社会有序运行的保障。然而每年由于农作物病虫害造成的损失巨大,因此及时精准地检测农作物病虫害情况并采取相应措施,对于农业发展有着重要意义。近年来,深度学习在图像识别方面取得巨大进展,其中卷积神经网络具有较好的图像识别能力,利用该技术可以准确地识别农作物病虫害,以便及时地进行防治。首先,该文分别综述农作物病虫害识别的传统方法、机器学习方法、深度学习方法,并分析比较3种方法的优缺点。其次,阐述国内外专家学者在农作物病虫害识别关键技术上的研究,分别分析总结数据集的获取途径和规模、数据集多种预处理技术的作用、数据集增强技术的多种方法、网络模型的迁移学习和预处理的作用、网络模型的种类和优缺点及网络模型多种优化技术的特点和优缺点。最后,指出目前基于卷积神经网络的农作物病虫害识别研究的热点难点,并对其应用前景进行展望。
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.
出处
《智慧农业导刊》
2024年第17期39-45,共7页
JOURNAL OF SMART AGRICULTURE
基金
安徽省高等学校科学研究项目(2023AH051867)
安徽省自然科学基金(2008085QF329)
安徽科技学院科研发展基金项目(811493)。
关键词
深度学习
卷积神经网络
图像识别
关键技术
病虫害识别
deep learning
Convolutional Neural Network
image recognition
key technology
pest identification
