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Camera trapping has advanced significantly in Australia over the last two decades. These devices have become more versatile and the associated computer technology has also progressed dramatically since 2011. In the USA, the hunting industry drives most changes to camera traps; however the scientific fraternity has been instrumental in incorporating computational engineering, statistics and technology into camera trap use for wildlife research. New survey methods, analytical tools (including software for image processing and storage) and complex algorithms to analyse images have been developed. For example, pattern and texture analysis and species and individual facial recognition are now possible. In the next few decades, as technology evolves and ecological and computational sciences intertwine, new tools and devices will emerge into the market. Here we outline several projects that are underway to incorporate camera traps and associated technologies into existing and new tools for wildlife management. These also have significant implications for broader wildlife management and research.

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This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for an ensemble learning algorithm. The Colour Feature Extreme Learning Machine (CF-ELM) is used in this study due to its additional ability to extract colour image features. The SCF-ELM is an ensemble learner that utilizes feature mapping via k-means clustering, a decision matrix and majority voting. It has been evaluated on a range of challenging agricultural object classification scenarios including weed, livestock and machinery detection. SCF-ELM model performance results were excellent both in terms of detection, 90 to 99% accuracy, and also inference times, around 0.01(s) per image. The SCF-ELM was able to compete or improve upon established algorithms in its class, indicating its potential for remote computing applications in agriculture.