Measuring the Flight Trajectory of a Free-Flying Moth on the Basis of Noise-Reduced 3D Point Cloud Time Series Data.

Abstract

Pest control is crucial in crop production; however, the use of chemical pesticides, the primary method of pest control, poses environmental issues and leads to insecticide resistance in pests. To overcome these issues, laser zapping has been studied as a clean pest control technology against the nocturnal cotton leafworm, Spodoptera litura, which has high fecundity and causes severe damage to various crops. For better sighting during laser zapping, it is important to measure the coordinates and speed of moths under low-light conditions. To achieve this, we developed an automatic detection pipeline based on point cloud time series data from stereoscopic images. We obtained 3D point cloud data from disparity images recorded under infrared and low-light conditions. To identify S. litura, we removed noise from the data using multiple filters and a support vector machine. We then computed the size of the outline box and directional angle of the 3D point cloud time series to determine the noisy point clouds. We visually inspected the flight trajectories and found that the size of the outline box and the movement direction were good indicators of noisy data. After removing noisy data, we obtained 68 flight trajectories, and the average flight speed of free-flying S. litura was 1.81 m/s.