LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction.

ABSTRACT: The desert locust is one of the most destructive locusts recorded in human history, and it has caused significant food shortages, monetary losses, and environmental calamities. Prediction of locust attacks is complicated as it depends on various environmental and geographical factors. This research aims to develop a machine-learning model for predicting desert locust attacks in 42 countries that considers three predictors: soil moisture, maximum temperature, and precipitation. We developed the Global Locust Attack Database for 42 countries (GLAD42) by integrating TerraClimate's environmental data with locust swarm attack data from the Food and Agriculture Organization (FAO). To improve the usability of spatial data, reverse geocoding which is the process of converting geographic coordinate

SUBMITTER: Khan S 

PROVIDER: S-EPMC11623073 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature