Insect classification using image processing and bayesian network
Hafiz Gulfam Ahmad Umar, Qaisar Abbas and Fatima Gulzar
In every discipline classification is a difficult job especially in the case of insect species. Outstanding high degree of resemblance of the appearance between distinct species classification becomes a problematic challenge. The present study discussed different techniques to classify insects especially using Bayesian network and proposed an insect identification scheme that can identify insect colored images. Four different classes of insects were selected for an evidence of concept. Classification was accomplished by manipulating insects’ shape feature and their histogram and color. Since each insect has different color and distinctive body shapes, so the current study also intend to propose a Bayesian classification approach that allows proficiently development of many valuable applications in vector control for both medical and agricultural entomology. Precisely classify insects by using proposed framework would have significant implications for entomological research. The present study classification model is very robust and fit for a general classification framework that permits to easily integrate arbitrary number of features.