使用修改后的Yolov5m模型与卷积神经网络进行番茄果实检测.
Fa-Ta Tsai1, Van-Tung Nguyen1, The-Phong Duong2
1Department of Mechanical Engineering, National United University, Miaoli 36002 Taiwan.
Plants (Basel, Switzerland)
|September 9, 2023
概括
自动化收获系统对于农业行业至关重要. 这项研究开发了一种高效的番茄检测模型,使用具有先进神经网络的Yolov5m,实现成熟,不成熟和受损水果的高精度.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 农业部门面临着劳动密集型和低效的收获过程带来的重大挑战.
- 越来越需要自动化系统来提高水果收获的效率和降低成本.
研究的目的:
- 提出和评估对象分类模型,用于自动检测番茄果实.
- 为农业行业加强自动化收获系统的开发.
主要方法:
- 通过将Yolov5m与BoTNet,ShuffleNet和GhostNet卷积神经网络 (CNN) 集成,开发了三种对象分类模型.
- 在1508张正常化图像上训练模型,其中包括三种类型的桃番茄:成熟的,不成熟的和受损的.
主要成果:
- 约洛夫5m + BoTNet模型显示了高检测准确度:成熟的西红94%;未成熟的西红95%;受损的西红96% .
- 提出的模型显示了在自动收获中实际应用的巨大潜力.
结论:
- 修改后的Yolov5m + BoTNet模型为开发先进的自动化番茄收获系统提供了一个有希望的基础.
- 这项研究有助于通过智能自动化解决水果采摘中人工劳动所面临的挑战.
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