Early Detection of Locust Swarms Using Deep Learning

Locust outbreaks have contributed to an upheaval of pre-harvest crop losses around the globe, especially in Sub-Saharan Africa, the Middle East and parts of South-Eastern Asia. They form due to intermittent geographical, biological and meteorological factors. Current methods of forecasting locust swarms involve periodic inspections that are manually conducted. This can be a time-consuming and laborious task. Satellite forecasting techniques have recently been implemented that improved the forecasting of formation of such swarms based on patterns of weather and soil data collected. However, locust plagues continue to form in certain regions causing large-scale damage to crop produce and creating discomfort among the inhabitants of these vulnerable areas. The recent improvements in the effectiveness of deep learning and computer vision algorithms can be used to improve the efficiency of spotting the formation of such swarms so that appropriate measures can be taken sooner. A model that makes use of convolutional neural networks that detects the presence of Locusts in the given setting and outputs a count of the insect present in the area is proposed. The outputs obtained show that the proposed deep learning implementation is accurate up to 83% and can be improved further.