# Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization
## 5 Summary and Outlook  
Deep learning methods show high potential for application in crystallization. They do not only outperform classical image processing methods based on hand-crafted features in connection with machine learning methods, they also enable a more detailed description of the particles. Beside a pixel-wise detection of crystals and classification into single crystals and agglomerates, CNNs also enable the quantification of primary crystals bound in agglomerates. This is not done by a regression or classification task anymore, instead also  
Figure 11: Number based and particle size distribution of primary crystals in agglomerates for \(\theta_{sat}=40\)\({}^{\circ}\)C in bright and \(\theta_{sat}=50\)\({}^{\circ}\)C in dark red. (**a**) Number distribution of primary crystals per agglomerate, (**b**) Particle size distribution of primary crystals in agglomerates. Figure 10: The average number of primary crystals per agglomerate (\(\overline{pc/ag}\)) as function of agglomerate size for \(\theta_{sat}=40\)\({}^{\circ}\)C in bright and \(\theta_{sat}=50\)\({}^{\circ}\)C in dark red. The error bars are calculated according to the three replicated crystallizations for each saturation temperature. the primary crystals can be detected and classified on a pixel-level. The characterization of the agglomeration can be extended to a deeper level this way. For the detection of the three crystal classes, a combination of two Mask-RCNNs has proven to be promising to be able to detect the primary crystals in agglomerates as precisely as possible. Especially big crystals can be evaluated accurately and also the main portion of primary crystals in agglomerates is quantified in a satisfying manner. The limitations of the methods mainly concern strongly agglomerated crystals and small particles in general. These challenges during image processing are not exclusively due to the method itself. After all, it is a very complex task that poses challenges even for the human user. The more detailed the image evaluation is and the more small particles are processed, the more subjective the assessment of the annotation becomes. To counteract the challenges, the image acquisition plays an important role. The particle characterization can be significantly improved by an adapted resolution and a better representation of the crystals where the contours of the individual primary crystals are more clearly visible. For characterization of agglomeration and quantification of primary crystals in agglomerates the method was applied for L-alanine crystallized from aqueous solution successfully. An increased saturation temperature leads to a longer crystallization time and higher solid content promoting agglomeration throughout the whole crystallization time. This is not only depicted by an increasing agglomeration degree, but also the level of agglomeration by an enhanced number of bounded primary crystals in agglomerates. Especially for bigger agglomerates the bounded primary crystals can be identified pixel-accurately. The primary crystals bound in an agglomerate are predominantly less lumpy and usually consist of fewer than five primary crystals, regardless of size. As it can be seen, this method can be used to describe the agglomeration behavior in detail and to build up a better understanding. Previously flow cell microscopy has already been integrated in a batch crystallization showing the evolution of crystals in the course of crystallization for a different model system adipic acid/water [12; 13]. Difficulties result in the operation, e.g., an influence on nucleation has been observed. Consequently further work will focus on integrating the image analysis into the process without interfering it and incorporating the deeper findings into modeling. Conceptualization experiments, J.L. and K.W. ; Conceptualization image processing, T.H., F.W. and J.L. ; Experimental study and evaluation, J.L. ; Set up, training and evaluation CNN, T.H. and J.L. ; Data collection, J.L. ; writing, J.L. and T.H.