Development of a Fruit Theft Reporting System Using a Compact Microcontroller with Deep Learning Based on Suspicious Sounds

In Japanese fruit-growing regions, fruit theft is a significant issue. Traditional anti-theft methods are fraught with various shortcomings and are often ineffective. To address this, we have developed a new approach for preventing fruit theft: a device that detects suspicious sounds by integrating a sound sensor with a compact, low-power microcontroller. This paper presents a suspicious sound detection system designed for a fruit theft alert device. It details the development of a deep learning model for detecting suspicious sounds, covering aspects from data collection to model training and evaluation. Despite operating on a microcontroller with limited memory and computational power, the system under development has achieved a classification accuracy of up to 78.5% in F1-score for footsteps, speech, and other environmental sounds.