An Automated and Scalable ML Solution for Mapping Invasive Species: the Case of the Australian Tree Fern in Hawaiian Forests

Abstract

Biodiversity loss and ecosystem degradation are global challenges demanding creative and scalable solutions. Recent increases in data collection coupled with machine learning have the potential to expand landscape monitoring capabilities. We present a computer vision solution to the problem of identifying invasive species. The Australian Tree Fern (Cyathea cooperi) is a fast growing species that is displacing slower growing native plants across the Hawaiian islands. The Nature Conservancy organization has partnered with Amazon Web Services to develop and test an automated tree fern detection and mapping solution based on imagery collected from fixed wing aircraft. We utilize deep learning to identify tree ferns and map their locations. Distinguishing between invasive and native tree ferns in aerial images is challenging for human experts. We explore techniques such as image embeddings and principal component analysis to assist in the classification. Creating quality training datasets is critical for developing ML solutions. We describe how semi-automated labeling tools can expedite this process. These steps are integrated into an automated cloud native inference pipeline that reduces localization time from weeks to minutes. We further investigate issues encountered when the pipeline is utilized on novel images and a decline in performance relative to the training data is observed. We trace the origin of the problem to a subset of images originating from steep mountain slopes and riverbanks which generate blurring and streaking patterns mistakenly labeled as tree ferns. We propose a preprocessing step based on Haralick texture features which detects and flags images different from the training set. Experimental results show that the proposed method performs well and can potentially enhance the model performance by relabeling and retraining the model iteratively.