基于深度成像的框架,用于在番茄果实中有效的表型识别
Junqing Li1, Guoao Dong1, Yuhang Liu2
1College of Information Science and Engineering, Shandong Agricultural University, Tai'an 271018, China.
Plants (Basel, Switzerland)
|November 27, 2025
概括
这项研究介绍了一种智能框架,用于使用计算机视觉和深度学习进行自动化番茄表型化. 该系统准确量化了12种水果特征,有助于精确的育种和栽培.
科学领域:
- 园艺科学 园艺科学
- 计算机视觉 计算机视觉 计算机视觉
- 人工智能的人工智能是人工智能.
背景情况:
- 番茄是一种至关重要的全球作物,需要精确的表型化来进行育种和质量控制.
- 当前的表型化方法可能是劳动密集型的,缺乏高精度.
- 基于计算机视觉的自动化方法为高效和准确的分析提供了潜力.
研究的目的:
- 开发一种智能检测框架,用于对番茄果实进行自动化现象学分析.
- 使用图像处理和深度学习提取和定量分析12种表型特征.
- 为精确的番茄育种和智能种植提供可靠的数据.
主要方法:
- 使用深度摄像头创建了一组番茄水果部分图像的数据集.
- 改进的SegFormer模型 (SegFormer-MLLA) 已被开发用于准确的水果表型细分.
- 一个混合深度回归模型融合了RGB和深度数据用于特征估计.
主要成果:
- 采用SegFormer-MLLA模型,可以在降低计算成本的情况下,准确地对番茄果实结构进行细分.
- 该框架准确地检测到关键的表型特征,如直径,厚度和茎痕尺寸.
- 实验结果显示,模型检测和手动测量的表型参数之间存在很高的相关性.
结论:
- 开发的框架准确有效地自动化了番茄果实表型.
- 该系统提供可靠的数据,支持精确的番茄育种和智能种植.
- 该方法作为其他水果作物表型的参考.
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