| Autor: |
Xiahui Fu, Juxia Wang, Fengzi Zhang, Weizheng Pan, Yu Zhang, Fu Zhao |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Horticulturae, Vol 10, Iss 3, p 275 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2311-7524 |
| DOI: |
10.3390/horticulturae10030275 |
| Popis: |
The colors of walnut fruits and leaves are similar in the oil transformation period, and the fruits are easily blocked by the branches and leaves. On the basis of the improved YOLOv7-tiny, a detection model is proposed and integrated into an Android application to solve the problem of walnut identification. Ablation experiments conducted with three improved strategies show that the strategies can effectively enhance the performance of the model. In terms of combinatorial optimization, the YOLOv7-tiny detection model that combines FasterNet and LightMLP modules works excellently. Its AP50 and AP50–95 are 3.1 and 4 percentage points (97.4% and 77.3%, respectively) higher than those of the original model. YOLOv7-tiny’s model size and number of parameters are reduced by 14.6% and 14.4%, respectively, relative to those of the original model, and its detection time decreases to 15.4 ms. The model has good robustness and generalization ability and can provide a technical reference for intelligent real-time detection of walnuts during the oil conversion period. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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