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用于植物表型分析的目标检测、尺寸和颜色测定的免费开源软件。

Free and open-source software for object detection, size, and colour determination for use in plant phenotyping.

作者信息

Wright Harry Charles, Lawrence Frederick Antonio, Ryan Anthony John, Cameron Duncan Drummond

机构信息

Department of Chemistry, The University of Sheffield, Sheffield, S3 7HF, UK.

Department of Chemistry, Imperial College London, London, SW7 2AZ, UK.

出版信息

Plant Methods. 2023 Nov 15;19(1):126. doi: 10.1186/s13007-023-01103-0.

Abstract

BACKGROUND

Object detection, size determination, and colour detection of images are tools commonly used in plant science. Key examples of this include identification of ripening stages of fruit such as tomatoes and the determination of chlorophyll content as an indicator of plant health. While methods exist for determining these important phenotypes, they often require proprietary software or require coding knowledge to adapt existing code.

RESULTS

We provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart. Further scripts identify objects, use an object of known size to calibrate for size, and extract the average colour of objects in RGB, Lab, and YUV colour spaces. We use two examples to demonstrate the use of these scripts. We show the consistency of these scripts by imaging in four different lighting conditions, and then we use two examples to show how the scripts can be used. In the first example, we estimate the lycopene content in tomatoes (Solanum lycopersicum) var. Tiny Tim using fruit images and an exponential model to predict lycopene content. We demonstrate that three different cameras (a DSLR camera and two separate mobile phones) are all able to model lycopene content. The models that predict lycopene or chlorophyll need to be adjusted depending on the camera used. In the second example, we estimate the chlorophyll content of basil (Ocimum basilicum) using leaf images and an exponential model to predict chlorophyll content.

CONCLUSION

A fast, cheap, non-destructive, and inexpensive method is provided for the determination of the size and colour of plant materials using a rig consisting of a lightbox, camera, and colour checker card and using free and open-source scripts that run in Python 3.8. This method accurately predicted the lycopene content in tomato fruit and the chlorophyll content in basil leaves.

摘要

背景

图像的目标检测、尺寸测定和颜色检测是植物科学中常用的工具。这方面的关键例子包括识别番茄等果实的成熟阶段,以及测定叶绿素含量作为植物健康状况的指标。虽然存在确定这些重要表型的方法,但它们通常需要专有软件,或者需要编码知识来改编现有代码。

结果

我们提供了一组免费的开源Python脚本,这些脚本无需任何改编,就能使用色卡对图像进行背景校正和颜色校正。进一步的脚本可识别目标、使用已知尺寸的物体进行尺寸校准,并提取RGB、Lab和YUV颜色空间中目标的平均颜色。我们用两个例子展示了这些脚本的用法。我们通过在四种不同光照条件下成像来展示这些脚本的一致性,然后用两个例子说明如何使用这些脚本。在第一个例子中,我们使用果实图像和指数模型来预测番茄(Solanum lycopersicum)品种Tiny Tim中的番茄红素含量。我们证明了三种不同的相机(一台数码单反相机和两部不同的手机)都能够对番茄红素含量进行建模。预测番茄红素或叶绿素的模型需要根据所使用的相机进行调整。在第二个例子中,我们使用叶片图像和指数模型来预测罗勒(Ocimum basilicum)的叶绿素含量。

结论

提供了一种快速、廉价、无损且成本低廉的方法,用于使用由灯箱、相机和色卡组成的装置,并通过在Python 3.8中运行的免费开源脚本,来测定植物材料的尺寸和颜色。该方法准确预测了番茄果实中的番茄红素含量和罗勒叶片中的叶绿素含量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e8b/10647133/5d4902a02c20/13007_2023_1103_Fig1_HTML.jpg

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