中国水稻科学 ›› 2019, Vol. 33 ›› Issue (1): 90-94.DOI: 10.16819/j.1001-7216.2019.8051

• 研究简报 • 上一篇    

基于卷积神经网络的水稻纹枯病图像识别

刘婷婷, 王婷, 胡林*()   

  1. 农业部农业大数据重点实验室/中国农业科学院 农业信息研究所,北京 100081;
  • 收稿日期:2018-04-23 修回日期:2018-10-16 出版日期:2019-01-10 发布日期:2019-01-10
  • 通讯作者: 胡林

Rhizocotonia Solani Recognition Algorithm Based on Convolutional Neural Network

Tingting LIU, Ting WANG, Lin HU*()   

  1. Key Laboratory of Agricultural Big Data,Ministry of Agriculture/Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081;
  • Received:2018-04-23 Revised:2018-10-16 Online:2019-01-10 Published:2019-01-10
  • Contact: Lin HU

摘要:

【目的】水稻纹枯病是影响水稻生产的三大病害之一。研究卷积神经网络对水稻纹枯病的自动识别,弥补人工识别的不足,对预防和准确识别水稻蚊枯病类型有着重要意义。【方法】以卷积神经网络进行水稻纹枯病识别,并与基于支持向量机的识别方法进行对比。【结果】卷积神经网络识别率达到97%,优于支持向量机的95%。【结论】卷积神经网络运用于水稻纹枯病识别是可行的,弥补了人工识别的不足。此算法训练的模型有着较好的识别性能。

关键词: 水稻纹枯病, 卷积神经网络, 分类识别

Abstract:

【Objective】Rice sheath blight is one of the three major diseases in rice production.The convolutional neural network which stands out for automatic identification of rice shealth blight can compensate for the lack of human identification. To solve this problem and prevent diseases deterioration, accurate identification of diseases types is of great significance.【Method】The convolutional neural network method was used to recognize rice sheath blight and compared with the recognition method based on support vector machine.【Result】The convolutional neural network method showed the recognition rate of 97%, better than that of support vector machine(95%).【Conclusion】The application of convolutional neural network to the identification of rice sheath blight is feasible and makes up for the lack of artificial recognition. The model trained by this algorithm has great recognition performance.

Key words: Rhizocotonia solani, convolutional neural network, classification and recognition

中图分类号: