使用深度学习算法检测在不同的照明条件下在室内生长的有缺陷的菜苗
Munirah Hayati Hamidon1, Tofael Ahamed2
1Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Japan.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
使用深度学习对有缺陷的生菜苗进行自动分类,显著提高了室内农业的效率. 在各种照明条件下,YOLOv7在检测受损幼苗方面取得了最高的准确性 (97.2%).
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 在室内农业中手动分类幼苗是劳动密集型的,容易出现错误,特别是在不同的照明条件下.
- 准确识别受损或有缺陷的苗木对于在受控环境中保持作物健康和产量至关重要.
研究的目的:
- 开发和评估基于深度学习的系统,用于自动检测有缺陷的生菜苗.
- 评估不同深度学习模型在各种室内种植照明条件下的性能.
主要方法:
- 在白色,蓝色和红色室内照明下捕获了生菜苗的图像.
- 包括CenterNet,YOLOv5,YOLOv7和Faster R-CNN在内的深度学习模型被用于缺陷检测.
- 在不同的照明场景中,使用平均平均精度 (mAP) 评估性能.
主要成果:
- YOLOv7表现出最高的检测准确度,平均精度 (mAP) 为97.2%.
- 在识别缺陷幼苗方面,YOLOv7的表现优于CenterNet (82.8%),YOLOv5 (96.5%) 和更快的R-CNN (88.6%).
- 在白色和红色/蓝色/白色组合照明条件下,YOLOv7的检测率更高.
结论:
- YOLOv7深度学习模型显示,在室内农业中,对有缺陷的菜苗进行分类和分类的自动化具有很大的潜力.
- 自动检测有缺陷的苗木可以提高苗木管理和整体室内农业操作的效率.
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