Name
Abstract | ||
|---|---|---|
The color information of diseased leaf is the main basis for leaf based plant disease recognition. To make use of color information, a novel three-channel convolutional neural networks (TCCNN) model is constructed by combining three color components for vegetable leaf disease recognition. In the model, each channel of TCCNN is fed by one of three color components of RGB diseased leaf image, the convolutional feature in each CNN is learned and transmitted to the next convolutional layer and pooling layer in turn, then the features are fused through a fully connected fusion layer to get a deep-level disease recognition feature vector. Finally, a softmax layer makes use of the feature vector to classify the input images into the predefined classes. The proposed method can automatically learn the representative features from the complex diseased leaf images, and effectively recognize vegetable diseases. The experimental results validate that the proposed method outperforms the state-of-the-art methods of the vegetable leaf disease recognition. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cogsys.2018.04.006 | Cognitive Systems Research |
Keywords | Field | DocType |
Vegetable leaf disease recognition,Feature extraction and selection,Convolutional neural networks (CNN),Three-channel CNN (TCCNN) | Feature vector,Softmax function,Pattern recognition,Convolutional neural network,Pooling,Communication channel,Psychology,RGB color model,Artificial intelligence,Machine learning | Journal |
Volume | ISSN | Citations |
53 | 1389-0417 | 3 |
PageRank | References | Authors |
0.37 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shanwen Zhang | 1 | 317 | 34.71 |
| Wenzhun Huang | 2 | 76 | 6.08 |
| Chuanlei Zhang | 3 | 51 | 10.12 |