Ecology is particularly ripe for the applications of deep learning owing to the increase in complex ecological datasets over the past few years ranging from
genomic to ecosystem-scale data, also known as Big Data \cite{White_2019,Hampton_2013,Farley_2018}. The Big data
derived from the increasingly sophisticated automatic monitoring by sensors can no
longer be manually processed as it is redundant and time
consuming \cite{Weinstein_2017,Norouzzadeh_2018}.
Deep learning is specifically better than other methods in dealing with non-linear
complex data commonly encountered in
ecology \cite{Christin_2019}. In fact, all winning methods for the most recent LifeCLEF
contests have been deep learning-based \cite{Joly_2017}. Reviews and proposals for these have been put forward and the field
feels right for disruption \cite{Christin_2019,Lamba_2019}. Deep
learning has been touted as a contender in solving problems with immediate application ranging from
illegal trafficking of wildlife products to large scale automated
ecosystem management tools - areas that are expensive and logistically
expensive to manage \cite{Cantrell_2017,Christin_2019}.
A lot of the challenges that prevented deep learning from having practical applications have been eliminated with g̶r̶o̶u̶n̶d̶b̶r̶e̶a̶k̶i̶n̶g̶ advancements research on transfer learning and
data augmentation \cite{Shorten_2019}. This has led to a reduction in the data required to make accurate world-class models. Furthermore, the recent wave in
computer hardware innovation for GPU’s and CPU’s has also accelerated by
reducing the cost of accessing the processing power required for
accurate model development.
Therefore, to illustrate to non-experts how they can prototype these previously mysterious techniques this paper takes you step by step on the various stages and offers open-source code in form of an annotated Jupyter Notebook that can be used by anybody in the world to produce expert-level accuracy on whatever supervised species classification they want to carry out. The tutorial is designed in a way that it can be implemented in the lowest resourced environment and unlock great application in taxa image identification in ecology the world over that we can hardly imagine at the moment.
T̶h̶i̶s̶ ̶r̶o̶a̶d̶m̶a̶p̶ ̶w̶a̶s̶ ̶p̶o̶p̶u̶l̶a̶r̶i̶z̶e̶d̶ ̶b̶y̶ ̶t̶h̶e̶ ̶f̶a̶s̶t̶.̶a̶i̶ ̶c̶o̶u̶r̶s̶e̶ ̶c̶r̶e̶a̶t̶e̶d̶ ̶b̶y̶ ̶J̶e̶r̶e̶m̶y̶ ̶H̶o̶w̶a̶r̶d̶ ̶a̶n̶d̶ ̶R̶a̶c̶h̶e̶l̶ ̶T̶h̶o̶m̶a̶s̶ ̶b̶o̶t̶h̶ ̶s̶c̶i̶e̶n̶t̶i̶s̶t̶s̶ ̶a̶t̶ ̶t̶h̶e̶ ̶U̶n̶i̶v̶e̶r̶s̶i̶t̶y̶ ̶o̶f̶ ̶S̶a̶n̶ ̶F̶r̶a̶n̶s̶i̶s̶c̶o̶.̶ ̶T̶h̶e̶ ̶c̶o̶d̶e̶ ̶c̶a̶n̶ ̶b̶e̶ ̶a̶c̶c̶e̶s̶s̶e̶d̶ ̶f̶r̶o̶m̶ ̶t̶h̶e̶ ̶J̶u̶p̶y̶t̶e̶r̶ ̶N̶o̶t̶e̶b̶o̶o̶k̶ ̶h̶e̶r̶e̶:̶ ̶h̶t̶t̶p̶s̶:̶/̶/̶b̶i̶t̶.̶l̶y̶/̶3̶9̶w̶o̶e̶L̶t̶ The code can be accessed from the Jupyter Notebook here:
https://bit.ly/39woeLt