基于扩展修复功能增强和共享参数头部的类疾病检测
Xu Guo1, Xingmeng Wang2, Wenhao Zhu2
1School of Big Data and Automation, Chongqing Chemical Industry Vocational College, Chongqing 401228, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
一个新的轻量级AI模型,YOLOv8n-DE,准确地检测果园中的类疾病. 这种先进的系统通过提高疾病识别效率和减少农药应用错误来改善精准农业.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 准确的类病的鉴定对于果园中有效的农药应用至关重要.
- 由于各种病变模式和复杂的环境,现有的模型在准确性和效率方面面临挑战.
研究的目的:
- 开发一个改进的,轻量级的基于YOLOv8的模型 (YOLOv8n-DE),用于增强类病的检测.
- 在现实世界果园环境中提高疾病识别的准确性和效率.
主要方法:
- 在YOLOv8框架内引入了DR模块用于功能增强和Detect_Shared架构用于参数效率.
- 在公共和定制果园收集的数据集上评估了YOLOv8n-DE模型.
主要成果:
- YOLOv8n-DE实现了97.6%的分类准确率,91.8%的回忆率和97.3%的mAP.
- 与YOLOv8.8相比,参数减少了48.17%,计算负载减少了59.26%,模型尺寸缩小了41.94%.
- 在挑战类植物疾病方面实现了90.4%的mAP,降低了分类/回归错误和假阳性/假阴性.
结论:
- YOLOv8n-DE为类植物疾病检测提供了卓越的性能和轻量级优势.
- 该模型支持通过有效和准确的果园疾病识别来推进精准农业.
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