Existing method of surveying supermicroplastics (fragments ≤ 350 µm) in sea remains a challenge. To this end, we propose a new method based on laser speckles and two submersible spheres. In simulation experiments, a 630 nm laser illuminated a cuvette containing polystyrene particles and zooplanktons producing speckles recorded by a CMOS camera. Speckles were analyzed to discriminate different sized polystyrene spheres (2 μm, 20 μm and 200 μm) and zooplanktons. As discrimination algorithms, difference of subsequent frames of the speckle movie and deep learning were investigated. Deep learning was found to be capable of distinguishing speckles from different particle sizes.
In recent years, it has been found that sound also has effect on plant growth and its yield with certain sound directing the seedling of corn toward the sound source and its ability in distinguishing stuttering of larvae from other sounds. However, methods investigating the effects of sound either take a long time or destructive. Here, we have used laser biospeckle, a non-destructive and non-contact technique to investigate the activities of an arugula plant (2-4 weeks old) under sounds of different frequencies of 0 Hz or control, 100 Hz, 1 kHz, 10 kHz. Laser biospeckle has been proved to be valid for investigating the aging of fruits, believed to be due to the movement of water, organelles etc.. Biospeckle activities were recorded for 20 sec at 15 fps following exposure to sounds for 1min. The correlation parameter (r) of biospeckle activity was used to characterize the activity of the plant with r being 1 for highly active and 0 for reduced plant activity. Sound level of 100 dB was the same for all frequencies. There was a clear difference in r between the control and other frequencies and r was lower than that of control indicating a reduction in the activity. Moreover, r for 100 Hz was found to be closer to control while at higher frequencies, r was much lower indicating a dependence of the activity on the frequency.
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