On the Use of Circadian Cycles to Monitor Individual Young Plants

Occlusion is a very common problem in computer vision. The presence of objects seen as overlapped under a camera negatively impacts object recognition, object counting or shape estimation. This problem is especially important in plant imaging because plants are very self-similar objects which produce a lot of self-occlusions. A possible way to disentangle apparent occlusions is to acquire the same scene from different points of view when the object is motionless. Such a realization is not necessary if the objects move themselves under the camera and thus offer different points of view for free. This is the case in plant imagery, since plants have their own natural movements, including the so-called circadian rhythms. We propose to use these movements to solve some self-occlusion problems with a set of simple yet innovative sampling algorithms to monitor the growth of individualized young plants. The proposed sampling methods make it possible to monitor the growth of the individual plants until their overlap is definitive. The gain is significant with an average maximum duration of observation increase from 3 days to more than 10 days by comparison with a sampling method that would stop when the first overlap occurs.

Automated summary

Occlusion is a common issue in computer vision, especially in plant imaging. We propose using innovative sampling algorithms to monitor the growth of individual plants and solve self-occlusion problems, allowing for longer observation durations.