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3D Leaf Tracking for Plant Growth Monitoring

Abstract : This article presents a 3D approach in plant growth monitoring and deals with the tracking of leaves of sunflower plants. Our aim is to compute time-series of individual leaf area, under water stress and control conditions. These data will then be used by biologists to study the drought resistance of various sunflower species. Our method to track the leaves in 3D has been evaluated on a set of 132 point clouds obtained via classical structure-from-motion techniques and multi-view stereo software. These 3D acquisitions have been performed on 12 sunflower plants (6 water-stressed, 6 well-watered) during a period of one month (11 measurement dates per sunflower plant). This method gives promising results for both conditions (water-stressed and well-watered), for different species and is able to follow the growth of the plants, as well as to detect new leaf emergence and leaf decay.
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https://hal.laas.fr/hal-01957628
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Submitted on : Monday, December 17, 2018 - 2:40:56 PM
Last modification on : Wednesday, March 25, 2020 - 8:50:04 AM
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  • HAL Id : hal-01957628, version 1

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William Gélard, Ariane Herbulot, Michel Devy, Pierre Casadebaig. 3D Leaf Tracking for Plant Growth Monitoring. 25th IEEE International Conference on Image Processing (ICIP 2018), Oct 2018, Athènes, Greece. 5p. ⟨hal-01957628⟩

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