一种基于改进的YOLOv12的轻量级智能分级方法,用于花
Bing Xu1, Zejie Ma1, Xueping Su1
1School of Computer, Guangdong University of Petrochemical Technology, Maoming, Guangdong, China.
Frontiers in plant science
|January 7, 2026
概括
这项研究介绍了LycheeGuard-Lite,这是一种轻量级的人工智能模型,用于检测花炭病. 它在降低计算成本的情况下实现了高精度,从而实现了高效,非破坏性的水果质量分类.
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:
- 炭水化物显著降低了李子的质量,需要有效的检测方法.
- 手动分级是低效和主观的,阻碍了有效的质量管理.
- 开发准确而轻量级的AI模型用于疾病检测对于自动分类至关重要.
研究的目的:
- 开发一种轻量级的人工智能模型,用于快速,非破坏性地检测和分类李子.
- 通过平衡高精度与降低计算复杂度来改进现有的检测框架.
- 提供可部署的解决方案,用于自动化收获后百合花质量评估.
主要方法:
- 提出了LycheeGuard-Lite,这是一个基于YOLOv12框架的轻量级模型.
- 集成的C3k2_Light模块具有深度可分离的卷积和C2PSA注意力机制.
- 使用加权卷积策略 (wConv2D) 来增强特征提取并降低复杂性.
- 在一个数据集上对该模型进行了评估,该数据集包括两种李子品种和三种严重程度的14576张图像.
主要成果:
- 实现了99.4%的mAP50检测准确度,用于花.
- 模型参数减少了12.8% (共219M个).
- 计算成本降低了29.3% (4.1个GFLOP).
结论:
- LycheeGuard-Lite提供了一个高度准确和计算高效的解决方案,用于自动识别病.
- 轻量化设计使该模型适合在收获后分类系统中实际部署.
- 这项研究为改善的质量管理和减少食物浪费提供了有价值的工具.
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