Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (2): 111-119.doi: 10.11871/jfdc.issn.2096-742X.2020.02.009

• Special Issue: Data Analysis Technology & Application • Previous Articles     Next Articles

Image Recognition of Agricultural Diseases Based on Deep Transfer Learning

Chen Lei,Yuan Yuan()   

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China
  • Received:2019-11-29 Online:2020-04-20 Published:2020-06-03
  • Contact: Yuan Yuan E-mail:yuanyuan@iim.ac.cn

Abstract:

[Objective] This paper focuses on the issue of image recognition of agricultural diseases and explores the integration of different machine learning methods under different data scales to improve the accuracy of agricultural disease image recognition. [Methods] Focusing on the problem of machine learning modeling under the condition of small scale of agricultural disease image data, the deep transfer learning method is introduced and some specific experiments are conducted to explore how to improve the modeling effect under the condition of small samples. [Results] On the high-quality agricultural disease image data set, the introduced deep transfer learning method can effectively improve the accuracy of agricultural disease image recognition. [Limitations] In the machine learning method based on deep neural networks, both the quality and the scale of agricultural disease images have certain influence on the modeling effect. In the future, we will further explore the modeling method with better universality in data quality and scale. [Conclusions] In the research of agricultural disease image recognition technology, the adaptation of deep transfer learning method can effectively improve the machine learning modeling effect and the final disease image recognition accuracy under the condition of small samples, which can provide good technical support for the subsequent construction of various agricultural disease image recognition systems.

Key words: image recognition, transfer learning, deep learning, agriculture diseases, big data