Paper Info
Title
High-Order Residual Convolutional Neural Network For Robust Crop Disease Recognition
Abstract
Fast(1) and robust recognition of crop diseases is the basis for crop disease prevention and control. It is also an important guarantee for crop yield and quality. Most crop disease recognition methods focus on improving the recognition accuracy on public datasets, but ignoring the anti-interference ability of the methods, which result in poor recognition accuracy when the real scene is applied. In this paper, we propose a high-order residual convolutional neural network (HOResNet) for accurate and robust recognizing crop diseases. Our HOResNet is capable of exploiting low-level features with object details and high-level features with abstract representation simultaneously in order to improve the anti-interference ability. Furthermore, in order to better verify the anti-interference ability of our approach, we introduce a new dataset, which contains 9,214 images of six diseases of Rice and Cucumber. This dataset is collected in the natural environment. The images in the dataset have different sizes, shooting angles, poses, backgrounds and illuminations. Extensive experimental results demonstrate that our approach achieves the highest accuracy on the datasets tested. In addition, when the input images are added to different levels of noise interference, our approach still obtains higher recognition accuracy than other methods.
Year
DOI
Venue
2018
10.1145/3207677.3277952
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE2018)
Keywords
DocType
Citations 
High-order residual convolutional neural network, crop disease recognition, robustness
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Weihui Zeng100.34
Miao Li200.34
Jian Zhang300.34
Lei Chen400.34
Sisi Fang500.34
Jingxian Wang600.34