Name
Abstract | ||
|---|---|---|
Tomato production is often threatened by the bites of several pests (mainly whiteflies and cotton bollworms). Pests exist throughout the tomato growing season, and it is necessary to detect and prevent these stubborn pests in time to reduce the economic losses caused by pests. Deep learning has been widely used to identify plant diseases and insect pests in recent years. The performance of the deep learning model is greatly affected by the network structure and hyperparameters, especially for hyperparameters, which often require manual participation in selection. To obtain suitable hyperparameters, this paper improves the fruit fly optimization algorithm and uses the improved algorithm to optimize the learning rate of the deep network. The experimental results show that the improved fruit fly optimization algorithm can search for a better learning rate. Among the 6 networks participating in the experiment, IResNet50 has an average diagnostic accuracy of 94.4% for seven tomato pests, which is better than other models. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.compag.2022.106805 | COMPUTERS AND ELECTRONICS IN AGRICULTURE |
Keywords | DocType | Volume |
Tomato pests diagnosis, Deep residual networks, Fruit fly optimization algorithm, Classification | Journal | 195 |
ISSN | Citations | PageRank |
0168-1699 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helong Yu | 1 | 0 | 1.35 |
| Jiawen Liu | 2 | 0 | 0.68 |
| Chengcheng Chen | 3 | 1 | 1.02 |
| Ali Asghar Heidari | 4 | 379 | 23.01 |
| Qian Zhang | 5 | 17 | 21.63 |
| Huiling Chen | 6 | 402 | 28.49 |