Name
Abstract | ||
|---|---|---|
The difference of leaf disease images within the class is large but the difference between the class is small, so it is very important to represent the features of the local area of the target. Moreover, the complex network occupies a large amount of computer memory and wastes a large amount of computing resources, which is difficult to meet the needs of low-cost terminals. This paper proposes a fine-grained disease categorization method based on attention network to solve the problem. In "Classification Model", attention mechanism is used to increase identification ability. "Reconstruction-Generation Model" were added during training and the "Classification Model" have to pay more attention to differentiate areas to find differences instead of paying more attention to global features. And adversarial loss was applied to distinguish the generated image from the original image to suppress the noise introduced by the "Discrimination Model". Due to the feature that "Reconstruction-Generation Model" and "Discrimination Model" are only used in training and do not participate in the operation of inference phase, which cannot increase the complexity of the model. Compared with the traditional classification network, the method of generalization ability enhancement further enhances the identification accuracy. And the method needs less memory but can achieve low performance terminal real-time identification of peach and tomato leaf diseases. And it can be applied in other crop disease identification fields with the similar application scenarios. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACCESS.2022.3167513 | IEEE ACCESS |
Keywords | DocType | Volume |
Feature extraction, Diseases, Image classification, Convolutional neural networks, Training, Task analysis, Crops, Fine-grained categorization, attention, adversarial loss, reconstruction, generation | Journal | 10 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Wu | 1 | 84 | 18.42 |
| Xian Feng | 2 | 0 | 0.34 |
| Guojun Chen | 3 | 0 | 0.34 |