Deep Learning Methodology for Early Detection and Outbreak Prediction of Invasive Species Growth

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 and scalable models. This paper details the techniques used for the novel development of deep, multi-dimensional Convolutional Neural Networks (CNNs) to detect the presence of IS in both 2D and 3D spaces, as well as the creation of geospatial Long Short-Term Memory (LSTMs) models to then accurately quantify, simulate, and project invasive species’ future environmental spread. Results from conducting training and in-field validation studies show that this new methodology significantly improves current EDRR methods, by drastically decreasing the intensity of manual field labor while providing a toolkit that increases the efficiency and efficacy of ongoing efforts to combat IS. Furthermore, this research presents scalable expansion into dynamic LIDAR and aerial detection of IS growth, with the proposed toolkit already being deployed by state parks and national environmental/wildlife services.