›› 2018, Vol. 30 ›› Issue (6): 1073-1081.DOI: 10.3969/j.issn.1004-1524.2018.06.26

• Agricultural Economy and Development • Previous Articles     Next Articles

Identification of navel orange lesions leaves based on parametric exponential non-linear residual neural network

YANG Guoliang, XU Nan*, KANG Lele, GONG Man, HONG Zhiyang   

  1. School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China
  • Received:2017-08-17 Online:2018-06-20 Published:2018-07-02

Abstract: In order to better identify navel orange leaf lesions. The paper proposed a parametric exponential nonlinear function asactivation function to improve the residual network and provided a new method of deep learning to identify lesions of navel orange leaves, which reduced the linear loss of rectification and improves the training effect. In this paper, CNN was used as a training tool, and navel orange leaf images were selected as training samples to discriminate the four types of diseased, deficient, normal and non-species. This new identification method had the great advantage of being convenient to use and more accurate for the identification of traditional plant diseases, and finally reached the accuracy rate of 97.18%-98.86%.

Key words: neural networks, navel orange, disease identification, deep learning, residual

CLC Number: