A lightweight rice pest detection algorithm based on improved YOLOv8
Yong Zheng, Weiheng Zheng, Xia Du · 2024
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Description
Yong Zheng, Weiheng Zheng and Xia Du present Rice-YOLO, a light model for detecting rice pests, in work from 2024. Fast, accurate pest spotting matters for control, yield and quality. Pests are hard to sort because classes look alike, ages differ within a class and field backgrounds are messy, so standard detectors often struggle.
Built on YOLOv8-N, their model adds an efficient detection head, deep supervision layers and an improved dynamic upsampling module. Tests used a large public pest dataset and a sixteen-class rice pest dataset built for this task. Rice-YOLO beat prior detection methods on mean average precision and F1 score in the reported experiments, while staying light enough for field use.
Catalog record
| CREATOR | Yong Zheng, Weiheng Zheng, Xia Du |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |