Fruit Fly Detection Based on Wingbeat Sound Using Embedded Artificial Intelligence

The highly invasive oriental fruit fly has caused significant agricultural losses worldwide. Electronic traps have been widely studied for fruit fly detection and counting. However, research focusing on applying acoustic sensors to identify fruit flies based on their wingbeat sound is currently lacking. This study focused on identifying trapped oriental fruit flies based on wingbeat sound data. An acoustic sensor was integrated into the funnel trap to record the wingbeat sounds of trapped flies along with ambient environmental noise. The trap was deployed in an apple orchard for two months to collect data. A spectrogram transformation and Mel-filter bank were applied to process the captured audio, generating two distinct sets of spectrogram images. A deep learning model based on convolutional neural network architecture was then designed and deployed on an ESP32 microcontroller to classify the wingbeat sounds of fruit flies and other environmental sounds. The trained model’s field experiment in the orchard showed that the model could classify the sound of fruit fly wingbeat in real-time audio streams with an accuracy of up to 96.86%. This demonstrates the practical applicability of the sound-sensor-based fruit-fly identification method. In addition, implementing the deep learning model on a microcontroller results in a compact, low-power, and cost-effective electronic trap. As a result, the compact design and low power consumption make this solution a promising approach for real-time monitoring and early pest detection in agricultural environments. However, its broader applicability requires further validation across more diverse datasets, longer deployment periods, and varying environmental conditions.

Automation trap; Acoustic sensor; CNN architecture; Embedded system; Pest detection

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