免费开源软件用于对象检测,尺寸和颜色的确定,用于植物表型
Harry Charles Wright1, Frederick Antonio Lawrence2, Anthony John Ryan3
1Department of Chemistry, The University of Sheffield, Sheffield, S3 7HF, UK. harry.wright@sheffield.ac.uk.
Plant methods
|November 15, 2023
概括
本研究介绍了用于植物科学图像分析的免费,开源的Python脚本,可实现精确的对象检测,大小和颜色确定. 该方法以非破坏性的方式预测番茄中的柳科和玄米中的叶绿素,为专有软件提供了具有成本效益的替代方案.
科学领域:
- 植物科学 植物科学
- 图像分析 图像分析
- 现型化 (Phenotyping) 是一种表现方式.
背景情况:
- 对象检测,尺寸和颜色的确定在植物科学中对于果实成熟度和植物健康等表型至关重要.
- 现有的方法通常依赖于专有软件或需要编码专业知识.
- 需要易于使用,适应的工具来进行植物图像分析.
研究的目的:
- 开发和展示一套用于植物图像分析的免费和开源Python脚本套件.
- 为了实现精确的对象检测,尺寸校准和颜色提取,而无需代码调整.
- 展示一种非破坏性的,具有成本效益的植物表型化方法.
主要方法:
- 使用ColorChecker图表进行背景和颜色校正.
- 开发了使用已知的对象进行对象识别和尺寸校准的脚本.
- 在RGB,Lab和YUV颜色空间中提取平均对象颜色.
- 应用指数模型来预测西红中的柳科和玄米中的叶绿素.
主要成果:
- 为自动化图像分析创建了免费和开源的Python脚本.
- 这些脚本成功地执行了背景和颜色校正,对象检测和尺寸校准.
- 能够准确预测番茄中的白甘含量和大理石中的叶绿素含量.
- 该方法在不同的照明条件和相机类型 (DSLR,手机) 中显示出一致性.
结论:
- 建立了一种快速,便宜,非破坏性和廉价的方法来确定植物材料的大小和颜色.
- 开发的Python脚本和硬件设备 (灯箱,摄像头,颜色检查器) 提供了一个可访问的解决方案.
- 该方法准确地预测了植物样本中的关键生物化学化合物 (甘,叶绿素).
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