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28 January 2010 Fully automatic leaf characterisation in heterogeneous environment of plant growing automation
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In the last decade, we have seen a tremendous emergence of genome sequencing analysis systems. These systems are limited by the ability to phenotype numerous plants under controlled environmental conditions. To avoid this limitation, it is desirable to use an automated system designed with plants control growth feature in mind. For each experimental sequence, many parameters are subject to variations: illuminant, plant size and color, humidity, temperature, to name a few. These parameters variations require the adjustment of classical plant detection algorithms. This paper present an innovative and automatic imaging scheme for characterising the plant's leafs growth. By considering a plant growth sequence it is possible, using the color histogram sequence, to detect day color variations and, then, to compute to set the algorithm parameters. The main difficulty is to take into account the automaton properties since the plant is not photographed exactly at the same position and angle. There is also an important evolution of the plant background, like moss, which needs to be taken into account. Ground truth experiments on several complete sequences will demonstrate the ability to identify the rosettes and to extract the plant characteristics whatever the culture conditions are.
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Gaël Chareyron, Jérôme Da Rugna, and Amaury Darsch "Fully automatic leaf characterisation in heterogeneous environment of plant growing automation", Proc. SPIE 7538, Image Processing: Machine Vision Applications III, 753804 (28 January 2010);

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