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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.

The classification of pollen grains images are currently done manually and visually, being a weariful task and predisposed to mistakes due to human exhaustion. In this paper, the authors introduce an automatic classification of 55 different pollen grain classes, using convolutional neural networks. Different architectures and hyperparameters were used to improve the classification result. Using the networks VGG16, VGG19, and InceptionV3, were obtained accuracy rates over 93.58%.

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Yeast counting is an important step in monitoring the fermentation process in sugarcane mills to optimize ethanol production. There is a need for a faster method to count viable cells in place of the fastidious and operator-dependent traditional method. In this paper, the application of a slightly modified version of the standard Circular Hough Transforms to automate the inoculated fermentation process of Saccharomyces cerevisae is reported. The results of several experiments with different preprocessing algorithms and parameter adjustments are presented. The resulting system will be part of a microbiological control procedure that is being developed to respond to Brazilian ethanol sugarcane mill's demands.