## 5 Results and Discussion  
### Developed Computer Vision Method Benefits and Drawbacks  
Figure 25 shows the comparison of number and total area surface of all detected bubbles via manual and automatic markup; while point-to-point stability of the result is rather low (up to 50%) one needs to remember that the initial data was measured by high-frequency camera and any single separate frame is not important in itself. The result of separate frames could be averaged later on in order to obtain the required trend information (Figure 14 shows more frames to estimate the spread). More important is the scale difference between manual and automatic markup. On average the effectiveness of automatic markup is only about 50% when assuming manual markup as 100%. The odds could be caused by a few factors:  
1. Manual markup overestimates the number of bubbles, especially small ones. Furthermore, manual markup tends to detect bubbles with different depths of view, whereas automatic markup is focused on a more narrow focus distance range.
2. The automatic procedure is just not sensitive enough, though the trend stability on Figure 14 is good enough.
3. Different parts of the frame have different lighting conditions and therefore different probability of detection. This assumption is partially confirmed by Figure 21a,b showing the distribution of \(x\) and \(y\) coordinates of automatically recognized bubbles. The distributions are expected to be uniform in the ideal case, and they are semi-uniform for labeled data but not for the automatic analysis.  
The first factor (overestimated number of bubbles in the field of view in manual markup) does not affect the total quality of research since it was used only for the preliminary results check. The real calibration of the automatic procedure will use the reference value of other experiments, not the manual markup. The global scaling factor includes several factors that could not be obtained from image analysis:  
* Geometry ratio between the observed window and the whole setup;
* Non-homogeneity of bubble distribution in the setup;
* The depth of field of the camera.  
The second factor (automatic markup sensitivity) could be improved by fine-tuning filter parameters. Those parameters must adjusted individually for each measurement setup. As a part of the future development of the project we plan to perform the automatic optimization of filter parameters in order to improve the recognition quality. Still it is not that crucial as long as recognition parameters are stable. The third factor (non-uniform lighting of the view field) should be improved by better light setup during the measurements. In the set of measurements external light was used which was revealed to be not sufficient for providing uniform lighting. In between the ways to circumvent the arising inconveniences when developing the project on the next stage, internal lights might be installed covering  
Figure 31: StarDist method application: bubble approximations by ellipses (**a**) and eccentricities of bubbles (**b**).  
only a narrow slice of the bioreactor volume (it would also improve the quality of the procedure as well), e.g., [47] proved itself with deserving results with a similar technique, but the initial data was declared in another way, i.e., this sort of measurement procedure was limited for the provided experiment due to the specific design of the apparatus).  
Another point of discussion is the comparison of _direct_ CV methods used in the current research with some more frequently used deep learning methods. The preliminary results presented in Section 4 are promising in terms of point-to-point stability but it requires additional study for different data sets as well as productivity analysis. Discovery and analysis of the _sufficient_ (in terms of control, safety and effectiveness of fermentation process) number of bubbles located on the frame as well as their optimal sizes, in other words defining the value of target parameter, is another sophisticated question requiring further research development.