Automatic insect recognition using optical flight dynamics modeled by kernel adaptive ARMA network
Automatic insect recognition (AIR), using noninvasive methods in situ, has far-reaching implications in entomology, agriculture, and disease control and prevention. An emerging technology in computational entomology uses flight information captured by laser sensors. Current methods treat these optical signals as static patterns, rather than time series. We propose a novel approach to AIR by evaluating each insect passage as a nonstationary process involving a sequence of pseudo-acoustic frames and modeling the short-term flight dynamics using the kernel adaptive autoregressive-moving average (KAARMA) algorithm. Since flight behavior is both nonlinear and nonstationary in nature, dynamic modeling provides a general framework that fully exploits the transitional and contextual information. Results show KAARMA classifier outperforms the state-of-the-art AIR methods, using support vector machine (SVM), deep-learning autoencoder, and batch learning, in identifying Zika vector mosquito Aedes aegypti among five species of flying insects, while using significantly more efficient data representation.