Empirical Mode Decomposition Applied to Acoustic Detection of a Cicadid Pest

The sounds emitted by various insect species are highly specific and, thus, can be used as a way to acoustically characterize them. Consequently, acoustic insect detection has been widely studied by the scientific community in the field of pattern recognition. In Brazil, the cicada species Quesada gigas is considered a pest in coffee plantations, because the insects feed on the sap of the plants and can cause losses to farmers in mass attacks. Based on the fact that the most striking feature of cicadas is the emission of sounds for breeding purposes, this paper presents an alternative algorithm for acoustic detection of cicadas. The algorithm combines sound feature extraction with feature analysis based on Empirical Mode Decomposition (EMD) and Paraconsistent Feature Engineering (PFE), respectively, followed by a classification step based on a Support Vector Machine (SVM). Specifically, a study on the influence of eight EMD stopping criteria on the classification of sounds is presented. The results show that the proposed methodology can obtain accuracy values above 98% considering the Energy Difference Tracking (EDT) stopping criterion, vectors with 18 features and at least 46% of the vectors for SVM training. In the computational cost aspect, the stopping criterion Standard Deviation (SD) stands out, providing accuracy values above 96.67% for vectors with only two features. These results show that this study is feasible for Internet of Things applications, favoring the development of detection devices for field use with long-lasting autonomy. Technologies like these can enable the implementation of more and more daring projects involving Smart Farms and e-waste, aiming to reduce impacts to the environment. Suggestions for future work based on the PFE are also presented.