A Thresholding Approach for Pollen Detection in Images Based on Simulated Annealing Algorithm

Melissopalynology is a field that studies pollen grain origins to identify their species. It consists of studying either the chemical composition of each grain, or their shapes using microscopic images. This paper presents a system of pollen identification based on the microscopic images, it is divided into two parts, first part is the pollen detection using a thresholding method with simulated annealing algorithm. The second step is the pollen classification, in which we used deep convolutional neural network to extract features from the detected pollen grains and represent them in numerical vectors, therefore, we can use these vectors to classify them based on fully connected neural network, SVM or similarity calculation. The obtained results showed a high efficiency of the neural network in which it could recognize 98.07% of the pollen species compared not just to SVM and similarity methods, but also to works from literature.