Detecting plant phenotypes from 3D point cloud data




Dani, Jimmy


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In recent years, with the rapid development in indoor plant genotyping, there is a growing need for precise quantification of plant phenotypes. Currently, manual plant phenotyping is being used which is laborious, time-consuming, and prone to errors. This served as a motivation to develop an automated greenhouse phenotyping framework, that uses a 3D point cloud generated from RGB images. This study is focused on variations in plant phenotypes on different genotypes namely, Atlantic and Olalla, under controlled and drought stress treatment, throughout the growing season. The phenotypes considered in this study are: plant height, plant volume, leaf angle distribution and Excessive Greenness Index. Images of the plant are taken from two cameras hung on a post and a 3D point cloud is generated from those images. The phenotypes derived from the point cloud showed high correlation with manual measurements, which shows the system could be used for a variety of indoor plant phenotyping. The 99 percentile height shows the highest correlation with manually estimated height, and the volume and excessive greenness index results shows the Olalla genotype is more susceptible to stress as compared to the Atlantic genotype, the leaf angle distribution shows higher wilting for the drought stress treatment as compared to the control treatment.



Genotyping, Leaf Angle Distribution, LiDAR, Phenotyping, Plant Volume, structure from motion



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