Smart Trap Design with Machine Learning and Embedded System Structure

It is very important to fight the Mediterranean fruit fly pest in orchards in our country. If this fight is not given, serious economic losses occur. Traps are currently being set up on trees to detect pests. It is important that these traps are checked by farmers at regular intervals in terms of pesticide applications of fruits. This process increases the workload of farmers. Therefore, a smart trap design that automatically detects the number of Mediterranean fruit flies is proposed in this study. The smart trap is implemented as an embedded system using the STM 32F746GDISCOVERY development board with an Arm Cortex M7 processor on it. Via the proposed system, quantitative data can be obtained about the population of Mediterranean fruit flies in the determined region. Machine learning is performed on the proposed embedded system. Therefore, a deep learning-based structure that can work on the embedded system has been designed. Obtained preliminary results indicate that the proposed system can be used for Mediterranean fruit fly detection and automatic determination of its number in a given region.