A Novel Method for Automated Identification and Prediction of Invasive Species Using Deep Learning

Invasive species (IS) cause major environmental damages, costing approximately $1.4 Trillion globally. Early detection and rapid response (EDRR) is key to mitigating IS growth, but current EDRR methods are highly inadequate at addressing IS growth. In this paper, a machine-learning-based approach to combat IS spread is proposed, in which identification, detection, and prediction of IS growth are automated in a novel mobile application. This paper details the use of Convolutional Neural Networks and geospatial Long Short-Term Memory models to identify and predict IS growth, with results from training and validation field studies significantly improving current EDRR methods.