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

Autor: Wright HC; Department of Chemistry, The University of Sheffield, Sheffield, S3 7HF, UK. harry.wright@sheffield.ac.uk., Lawrence FA; Department of Chemistry, Imperial College London, London, SW7 2AZ, UK., Ryan AJ; Department of Chemistry, The University of Sheffield, Sheffield, S3 7HF, UK., Cameron DD; Department of Earth and Environmental Sciences and Manchester Institute of Biotechnology, The University of Manchester, John Garside Building, Manchester, M1 7DN, UK. duncan.cameron@manchester.co.uk.
Jazyk: angličtina
Zdroj: Plant methods [Plant Methods] 2023 Nov 15; Vol. 19 (1), pp. 126. Date of Electronic Publication: 2023 Nov 15.
DOI: 10.1186/s13007-023-01103-0
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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