根据当地和全球特征融合,同时识别番茄品种和成熟度
Shaohuang Bian1, Jun Zhou1, Qinxiu Gao1
1College of Information and Electronic Engineering, China Agricultural University, Beijing 100083, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究引入了一种改进的YOLOv8n模型,用于准确识别番茄品种和成熟度,即使有遮. 这种新的方法在复杂的生长条件下提高了检测精度和效率.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 番茄品种和成熟度分类对于作物评价至关重要,但由于叶子封闭等环境因素而具有挑战性.
- 现有的方法在复杂的现场条件下难以获得准确性和效率.
研究的目的:
- 开发一种创新的,同时检测番茄品种和成熟度的模型.
- 在复杂的农业环境中克服当前识别方法的局限性.
主要方法:
- 一个改进的YOLOv8n模型,结合了频率适应扩展卷积 (FADC) 和高级选特征路径聚合网络 (HSPAN) 来实现特征融合.
- 整合频道关注和功能选择机制,以加强本地和全球功能整合.
- 利用强大的IoU (PIoU) 损失函数和动态检测头,以提高界限盒准确性和自适应特征提取.
主要成果:
- 拟议的模型展示了卓越的全球感知能力.
- 与其他评估的模型相比,实现了最高的检测准确性.
- 显示较低的计算复杂性,表明效率.
结论:
- 开发的模型有效地解决了番茄品种和成熟度检测方面的挑战.
- FADC,HSPAN,PIoU损失和动态检测头的组合显著提高了识别精度和效率.
- 这种方法为自动化农业监测和产量评估提供了一个有希望的解决方案.
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