Embedded System-Based Sticky Paper Trap with Deep Learning-Based Insect-Counting Algorithm

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one most challenging tasks in image processing. With aid machine vision learning, traditional (manual) can be automated. To achieve this goal, a particular data acquisition device an accurate recognition algorithm (model) necessary. In work, we propose new embedded system-based trap with OpenMV Cam H7 microcontroller board, which used anywhere field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). addition, deep learning-based insect-counting method where offer solutions for problems such as “lack data” “false detection”. By means proposed method, spraying (pest swarming) could then accurately scheduled.