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

TL;DR

This study developed a method to track the flight paths of moths using 3D point cloud data to improve laser zapping for pest control.

Contribution

A novel automatic detection pipeline using noise-reduced 3D point cloud time series data for tracking free-flying moths.

Findings

The average flight speed of free-flying Spodoptera litura was measured at 1.81 m/s.

Outline box size and directional angle were effective indicators for identifying noisy data in 3D point cloud time series.

Abstract

Pest control plays an important role in crop production. The cotton leafworm, Spodoptera litura, is well recognized as a pest that causes severe damage to a wide variety of crops. Because S. litura is nocturnal, it is challenging to control this species effectively. Recently, laser zapping has gained attention as a clean technology to control pest insects. It is important to precisely identify and predict the flight trajectories of free-flying moths under low-light conditions for better sighting during laser zapping. In this study, we developed an automatic detection pipeline based on point cloud time series data from stereoscopic images. Three-dimensional point cloud data were extracted from disparity images recorded under infrared and low-light conditions. We computed the size of the outline box and the directional angle of the 3D point cloud time series to remove noisy point clouds. We visually inspected the flight trajectories and found that the size and direction of the outline box were good indicators of the noisy data. Finally, we obtained 68 flight trajectories, and the average flight speed of free-flying S. litura was found to be 1.81 m/s.

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.

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