## 4 Conclusions  
The biospeckle activity of healthy and infested fruits was found to be different as shown by graphical and numerical results of activity analyses. Activity of healthy fruits was low and increased with infestation development, but spatial distribution of oviposition sites, which were the source of biospeckle activity, made numerical analysis difficult. Regions punctured by the ovipsitor showed higher activity than those that were not infested; thus, heterogeneity of intensities occurred with local peaks of activity spatially distributed in the biospeckle image. As a result of testing a qualitative approach to analyse biospeckle activity, it turned out that the GD method worked well in identifying infested areas. Out of numerical indicators of biospeckle activity calculated on the basis of the THSP image, only the biospeckle activity index, and correlation with THSP, proved to distinguish healthy fruits from those in the early stage of infestation. To provide a good representation of activity it is advisable to use a strategy aimed at random selection of pixels gathered around the global maximum of biospeckle activity or, if it is required, around a few local maxima. The potential of laser speckle analysis as a method for identification of highbush blueberry fruits infested by \(D\). _suzuki_ was confirmed.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Activity** & \multicolumn{3}{c}{**F0**} & \multicolumn{3}{c}{**F24**} & \multicolumn{3}{c}{**F72**} \\ \cline{2-10}
**Indicator** & **Mean** & **Median** & **Mean Rank** & **Mean** & **Median** & **Mean Rank** & **Mean** & **Median** & **Mean Rank** \\ \hline AVD & 1.776 & 1.906 & 14.400 \({}^{\text{a}}\) & 2.534 & 2.152 & 18.333 \({}^{\text{a}}\) & 8.421 & 5.660 & 36.267 \({}^{\text{b}}\) \\ IM & 8.260 & 7.496 & 13.400 \({}^{\text{a}}\) & 24.279 & 12.010 & 19.267 \({}^{\text{a}}\) & 272.559 & 77.261 & 36.333 \({}^{\text{b}}\) \\ RVD & \(-\)0.004 & \(-\)0.003 & 26.800 \({}^{\text{a}}\) & 0.001 & \(-\)0.005 & 21.533 \({}^{\text{a}}\) & 0.013 & 0.006 & 20.667 \({}^{\text{a}}\) \\ NAD & 0.010 & 0.010 & 12.933 \({}^{\text{a}}\) & 0.015 & 0.011 & 20.533 \({}^{\text{a}}\) & 0.041 & 0.033 & 35.533 \({}^{\text{b}}\) \\ BAI & 0.004 & 0.003 & 9.467 \({}^{\text{a}}\) & 0.014 & 0.014 & 22.267 \({}^{\text{b}}\) & 0.076 & 0.071 & 37.267 \({}^{\text{c}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic statistics for biospeckle indices calculated for THSP obtained from a random set of points follow a Gaussian distribution.  
Further studies should be conducted to improve the activity measurement protocol and search for technical solutions enabling application of biospeckle analysis in automation of the pest identification process at the post-harvest stage.  
Conceptualization, M.J.-M.; methodology, M.J.-M., A.R. and J.S.; software, A.R.; validation, A.R., M.J.-M. and J.S.; formal analysis, M.J.-M. and A.R.; investigation, M.J.-M. and J.S.; resources, J.S.; data curation, A.R. and J.S.; writing--original draft preparation, M.J.-M., A.R. and J.S.; writing--review and editing, M.J.-M. and A.R.; visualization, A.R.; supervision, M.J.-M.; project administration, M.J.-M.; funding acquisition, M.J.-M. and J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.