Rapid laser pest control system with 3D small object detection

In this study, a smart rapid laser scanning system with 3D small object detection for disabling caterpillars has integrated. A monocular camera vision system was developed which works in tandem with rapid laser scanning system. Two caterpillar species Orgyia Postica and Porthesia Taiwana were considered and their original images were used to train YOLO for identification. Color transform from RGB to HSV was applied to Orgyia Postica, and for Porthesia Taiwana, RGB color space was maintained, to successfully detect the caterpillar’s head. The centre of the caterpillar’s head was positively approximated by using k-means clustering algorithm. These identified coordinates were then exposed to automatically controlled laser beam. Compact CW lasers of wavelength 450 nm with 1.738 W power with beam diameter 2.5 mm was used and their respective effects were studied. The entire setup was controlled using the NVIDIA Jetson TX2 embedded system. It was observed that even a precise exposure of second long laser beams on the head incapacitated the caterpillar from further ingestion of food. Therefore, this synergistic utilization of deep learning and lasers seems to be a beneficial and promising approach to effectively control the pest population, thereby preventing crop damage and improving the yield.