Pollen morphology of 20 species belonging to the 14 genera of the family Gramineae has been investigated using light microscope and scanning microscope. It is a stenopalynous in nature. Pollen grains mostly spheroidal, monoporate rarely diporate, ±circular, pores small operculate, non-operculate, annulate or non-annulate. Tectum areolate to scabrate. On the basis of exine ornamentations 5 disti...

The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this da...

The automation of palynology (the identification and counting of pollen grains and spores) will be a small step for image recognition, but a giant stride for palynology. Here we show the first successful automated identification, with 100% accuracy, of a realistic number of taxa. The technique used involves a neural network classifier applied to surface texture data from light microscope images...

 In this study, the palynology of 33 species of Silene belonging to 15 sections was investigated to examine the correspondence between palynology, morphological sections and phylogenetic trees. The pollen grains were prepared using the acetolysis method. Quantitative and qualitative micromorphological features of pollens were investigated using light microscopy and scanning electron microscopy....

Pollen grains can provide valuable information to forensic palynology, such as the time of death or possible origin a corpse. Forensic Palynology is vital tool be used in criminal investigation because different environment has distinct pollen signatures. Brazil rich and diversified flora that suitable for application palynology. The purpose this research introduce palynology automation improve...

Pollen grains are microscopic so their identification and quantification has, for decades, depended upon human observers using light microscopes: a labour-intensive approach. Modern improvements in computing and imaging hardware and software now bring automation of pollen analyses within reach. In this paper, we provide the first review in over 15 yr of progress towards automation of the part o...