Automated identification of plants and animals have improved considerably in the last few years, in particular thanks to the recent advances in deep learning. The next big question is how far such automated systems are from the human expertise. Indeed, even the best experts are sometimes confused and/or disagree between each others when validating visual or audio observations of living organism. A picture or a sound actually contains only a partial information that is usually not sufficient to determine the right species with certainty. Quantifying this uncertainty and comparing it to the performance of automated systems is of high interest for both computer scientists and expert naturalists. This chapter reports an experimental study following this idea in the plant domain. In total, 9 deep-learning systems
Automated identification of plants and animals has improved considerably in the last few years, in particular thanks to the recent advances in deep learning. In order to evaluate the performance of automated plant identification technologies in a sustainable and repeatable way, a dedicated system
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