Abstract
Phenotyping aims to measure traits of interest, such as leaves, and instance-segmentation of organs is a crucial prerequisite for plant phenotyping. This paper evaluates whether deep learning methods (such as Mask R-CNN) has generality for segmentation of plant organs. Training was conducted using the Arabidopsis and Physalis. Our results show that the segmentation accuracy of the Mask R-CNN model is more than 70% across different growth periods of Arabidopsis and Physalis, which indicates that Mask R-CNN displays satisfying versatility for plant phenotyping and has high value for plant phenotyping. Furthermore, taking advantage of multi-view images, a leaf tracking method is presented to solve the problem of plant occlusions. The results show that the proposed method is promising to build a dynamic modeling for various plants during their entire growth cycles.
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Acknowledgements
We acknowledge funding from BBSRC NCG Grant Ref: BBS/E/W/0012844A and the Technological Innovation Project of Postgraduate Student of Chongqing (No. 2021S0049). We would like to thank the members of the National Plant Phenomics Centre of IBERS of Aberystwyth University for providing plant material and help on image acquisition.
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Lv, H., Chen, Z., Mo, Y., Lou, L., Song, R., Doonan, J.H. (2022). Segmentation and Counting of Plant Organs Using Deep Learning and Multi-view Images. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_36
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