在复杂的果园环境中检测小果果的注释图像数据集.
Dandan Wang1,2, Bo Wang3
1College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi, China.
Frontiers in plant science
|January 21, 2026
概括
一个包含2517张图像的新数据集有助于智能稀释系统检测小果. 该资源支持自动化果种植,提高效率和水果质量.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 园艺园艺 园艺园艺
背景情况:
- 水果生产的自动化系统需要高质量的数据集来准确检测.
- 检测小水果对于预先稀释过程至关重要,以优化产量和质量.
- 现有的数据集可能缺乏足够的变化,以在不同的果园条件下进行强大的小果检测.
研究的目的:
- 引入一套新型数据集,专门用于在研磨前的应用中检测小果.
- 为培训和验证智能稀释系统提供标准化资源.
- 促进果行业在自动化水果生产方面的进步.
主要方法:
- 在各种现实世界果园条件下 (天气,照明,水果大小3-25mm) 收集了2517张RGB图像.
- 从原来的3024x3024分辨率将图像标准化为500x500像素.
- 使用LabelImg软件用PASCAL VOC (XML) 和YOLO (TXT) 界限框格式对小果目标进行注释.
主要成果:
- 该数据集有效地支持各种物体检测架构的训练和验证 (例如,更快的R-CNN,YOLO,RT-DETR).
- 实验证实了数据集在证明多个检测模型性能方面的实用性.
- 有注释的数据使得在指定尺寸范围内精确地定位小果.
结论:
- 引入的小果预稀释数据集是开发自动化稀释系统的宝贵资源.
- 这一数据集可以大大提高果种植的稀释效率和整体水果质量.
- 这些数据的可用性促进了农业自动化领域的进一步研究和开发.
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