Applications for deep learning in ecology

TLDR

Deep learning has recently gained hype and has revolutionized fields like bioinformatics and medicine by handling large, complex datasets, suggesting its potential for ecology as datasets grow. This paper reviews existing deep‑learning implementations and demonstrates their successful use for species identification, animal‑behaviour classification, and biodiversity estimation in camera‑trap images, audio recordings, and videos. The authors outline the steps to build a deep‑learning network, discuss available tools, data and computing requirements, and provide guidelines, recommendations, and a reference flowchart to help ecologists adopt the technology. Deep learning proves beneficial across ecological disciplines, especially in management and conservation, and offers a powerful solution for automatically processing the vast amounts of monitoring data that humans cannot efficiently handle.

Abstract

Abstract A lot of hype has recently been generated around deep learning, a novel group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning has revolutionized several research fields such as bioinformatics and medicine with its flexibility and ability to process large and complex datasets. As ecological datasets are becoming larger and more complex, we believe these methods can be useful to ecologists as well. In this paper, we review existing implementations and show that deep learning has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. We demonstrate that deep learning can be beneficial to most ecological disciplines, including applied contexts, such as management and conservation. We also identify common questions about how and when to use deep learning, such as what are the steps required to create a deep learning network, which tools are available to help, and what are the requirements in terms of data and computer power. We provide guidelines, recommendations and useful resources, including a reference flowchart to help ecologists get started with deep learning. We argue that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.