基于多策略的多环境检测模型的优化对番茄生长点的芽进行了改进 YOLOv8
Jiang Liu1, Jingxin Yu2, Changfu Zhang1
1College of Mechanical and Electrical Engineering, Xi'an Technological University, Xi'an, 710021, China.
Scientific reports
|July 16, 2025
概括
这项研究引入了一种增强的YOLOv8模型,用于在温室中准确检测番茄花. 改进的模型实现了高精度和高效率的精密农业应用.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 检测番茄生长点和花对于产量质量至关重要,但在复杂的温室环境中具有挑战性.
- 现有的模型经常与生长条件的变化和视觉混乱作斗争.
- 为了高效的精准农业,需要自动化监测系统.
研究的目的:
- 开发了一种改进的YOLOv8模型,用于对番茄花进行强大而准确的检测.
- 为了提高模型在多样化和杂乱的设施环境中的性能.
- 为自动化番茄监控提供高效,轻量级的解决方案.
主要方法:
- 构建了一个全面的数据集,在10个生长条件中增强了注释.
- 集成了一个SE注意力模块,以改善杂乱场景中的特征表示.
- 用GhostConv取代标准卷积,以减少计算负载 (19%).
- 实现了一个适应规模的WIoU_v2损失函数,以实现优化梯度分配.
- 进行了废弃实验,以验证修改的协同效应.
主要成果:
- 通过协同修改实现了97.8%的mAP@0.5和85.1%的mAP@0.5:0.95.
- 减少了11%的模型参数,同时保持了高性能.
- 在农业机器人的实际温室部署中证明了93.6%的检测准确度.
- 验证了对规模和环境变化的改进适应能力.
结论:
- 增强的YOLOv8模型为番茄花检测提供了显著的改进.
- 该系统平衡了准确性,速度和轻量级设计,以实现实际的精密农业.
- 这为商业温室的自动监控提供了立即适用的解决方案.
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