Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps

Throughout the history, insects have had an intimate relationship with humanity, both positive and negative. Insects are vectors of diseases that kill millions of people every year and, at the same time, insects pollinate most of the world's food production. Consequently, there is a demand for new devices able to control the populations of harmful insects while having a minimal impact on beneficial insects. In this paper, we present an intelligent trap that uses a laser sensor to selectively classify and catch insects. We perform an extensive evaluation of different feature sets from audio analysis and machine learning algorithms to construct accurate classifiers for the insect classification task. Support Vector Machines achieved the best results with a MFCC feature set, which consists of coefficients from frequencies scaled according to the human auditory system. We evaluate our classifiers in multiclass and binary class settings, and show that a binary class classifier that recognizes the mosquito species achieved almost perfect accuracy, assuring the applicability of the proposed intelligent trap.