Plant Identification with Deep Learning Ensembles in ExpertLifeCLEF 2018 Sara Atito 1 , Berrin Yanikoglu 1 , Erchan Aptoula 2 , ˙ Ipek Ganiyusufo˘ glu 1 , Aras Yıldız 1 , Kerem Yıldırır 1 , Barı¸ s Sevilmi¸ s 1 , and M. Umut S ¸en 1 1 Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey 2 Institute of Information Technologies, Gebze Technical University, Kocaeli, Turkey {saraatito,berrin}@sabanciuniv.edu eaptoula@gtu.edu.tr Abstract. This work describes the plant identification system that we submitted to the ExpertLifeCLEF plant identification campaign in 2018. We fine-tuned two pre-trained deep learning architectures (SeNet and DensNetwork) using images shared by the CLEF organizers in 2017. Our main runs are 4 ensembles obtained with different weighted combinations of the 4 deep learning architectures. The fifth ensemble is based on deep learning features but uses Error Correcting Output Codes (ECOC) as the ensemble. Our best system has achieved a classification accuracy of 74.4%, while the best system obtained 86.7% accuracy, on the whole of the official test data. This system ranked 4th place among all the teams, but matched the accuracy of one of the human experts. Keywords: plant identification, deep learning, convolutional neural net- works 1 Introduction Automatic plant identification is the problem of identifying the given plant species in a given photograph. Plant identification challenge of the Conference and Labs of the Evaluation Forum (CLEF) [1,2,3,4,5,6,7,8] is the most well- known annual event that benchmarks the progress in identification of plant species. The campaign has been running since 2011, with plant species reaching 10,000 classes in the 2017 evaluation. The emphasis of the campaign changes slightly from year to year, while the core of the campaign is to benchmark plant identification progress. This year’s emphasis was on measuring automatic systems’ performances with that of human experts. For that reason, a subset of the test data was labelled by human experts and the systems were evaluated on their accuracy on the whole test set, as well as their performance on the subset. The details of the plant identification and the overall LifeCLEF campaigns are described in [8] and [9] respectively. We have been participating into this campaign since 2011, first with tradi- tional approaches and carefully selected features [10,11,12] and then with deep