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Arbuscular Mycorrhizae (AM) are mutualistic associations between Arbuscular Mycorrhizal Fungi (AMF) and the roots of many plant species. AMF spores give rise to filaments that develop in the root system of plants and contribute to the absorption of water and some nutrients. This article introduces a semi-automated counting model of AMF spores in slide images based on Artificial Neural Network (ANN). The semi-automated counting of AMF spores facilitates and accelerates the tasks of researchers, who still do the AMF spore counting manually. We built a representative database of spore images, processing images through the Circle Hough Transform (CHT) method and training an ANN to classify patterns automatically. The classification analysis and the performances of the proposed method against the manual method are presented in this paper. The accuracy for the identification of spores by CHT in conjunction to ANN classification in the images was 90%. The results indicate that this method can accurately detect the presence of AMF spores in images as well as count them with a high level of confidence.

The mathematical morphology presents a systematic model to extract geometrical characteristics of images using morphological operators, which transforms the original image into another, using a third image called structural element. The fuzzy mathematical morphology extends the morphological operators to grayscale and coloured images using the fuzzy logic, where the definition of the operators are defined using the concepts of implications and fuzzy conjunctions, specifically, the implications and conjunctions of Lukasiewicz. In this paper it was proposed a counting method of mycorrhizal fungi spores which are derived from mycorrhizas, a symbiotic association between a fungus and a plant, using fuzzy morphological operators. The counting of these spores was done manually using corrugated plate and the aid of a stereoscopic microscope.

Este trabalho apresenta um método de contagem de esporos de fungos micorrízicos a partir de imagens digitais usando morfologia fuzzy para separação de esporos e algoritmos desenvolvidos no Scilab para quantificação. O objetivo é mostrar a eficácia da morfologia fuzzy e as funções desenvolvidas na separação e contagem. A estratégia metodológica desenvolvida foi utilizar as definições de morfologia fuzzy desenvolvidas por Andrade et al e produzir um algoritmo capaz de separar e contar fungos. Espera-se obter um método capaz de realizar uma contagem eficiente.