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Pollen grains can provide valuable information to forensic palynology, such as the time of death or the possible origin of a corpse. Forensic Palynology is a vital tool to be used in a criminal investigation because the different environment has distinct pollen signatures. Brazil has a rich and diversified flora that is suitable for the application of forensic palynology. The purpose of this research is to introduce palynology automation as a tool to improve the investigative method in forensic palynology and apply it to forensic palynology automation. The studied city has different vegetation types, in which we performed assessments to identify its correspondent pollen types. PALINOVIC algorithm was developed using computer vision and geotechnology techniques. Our results show that it is possible to correlate pollen grains found in forensic samples by automatic pollen identification and with a mapping of the likely vegetation. Our results show that it is possible to relate the presence of pollen grains found in forensic samples through the automatic identification of images together with a database of georeferenced plant species. It was possible to analyze the pollen grains collected in eight bodies, where the algorithm presented a performance of 90.51% in the pollen grain classification tests. Furthermore, pollen grains could be correlated with the type of vegetation where the body was found. Thus, the technique developed can be applied in other urban centers from a previous georeferencing of plants, as well as a pollen database.

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The objective of this paper is to investigate which combination of parameters for the fingerling counting software results in the smallest Mean Absolute Error (MAE) and smallest Root Mean Squared Error (RMSE). For this, an image dataset called FISHCV155V was created and separated into training and test sets, where different combinations of parameters for the software were tested. From the obtained results were extracted individual performance metrics for each combination of parameters, such as MAE, Mean Square Error (MSE) and RMSE. Video frames were analysed comparing the parameter combination that obtained the best and worst results, in order to investigate the influence of such parameters in the performance of the software. From such results, it was concluded that the best combination reached 5.99 MAE and 9.96 RMSE.