Title:	Plant recognition by AI: Deep neural nets, transformers, and kNN in deep embeddings
Authors:	Picek, Lukáš Šulc, Milan Patel, Yash Matas, Jiří
Citation:	PICEK, L. ŠULC, M. PATEL, Y. MATAS, J. Plant recognition by AI: Deep neural nets, transformers, and kNN in deep embeddings. Frontiers in Plant Science, 2022, roč. 13, č. September, s. 1-16. ISSN: 1664-462X
Issue Date:	2022
Publisher:	Frontiers Media S.A.
Document type:	článek article
URI:	2-s2.0-85139567145 http://hdl.handle.net/11025/51180
ISSN:	1664-462X
Keywords in different language:	plant;species;classification;recognition;machine learning;computer vision;species recognition;fine-grained
Abstract in different language:	The article reviews and benchmarks machine learning methods for automatic image-based plant species recognition and proposes a novel retrieval-based method for recognition by nearest neighbor classification in a deep embedding space. The image retrieval method relies on a model trained via the Recall@k surrogate loss. State-of-the-art approaches to image classification, based on Convolutional Neural Networks (CNN) and Vision Transformers (ViT), are benchmarked and compared with the proposed image retrieval-based method. The impact of performance-enhancing techniques, e.g., class prior adaptation, image augmentations, learning rate scheduling, and loss functions, is studied. The evaluation is carried out on the PlantCLEF 2017, the ExpertLifeCLEF 2018, and the iNaturalist 2018 Datasets-the largest publicly available datasets for plant recognition. The evaluation of CNN and ViT classifiers shows a gradual improvement in classification accuracy. The current state-of-the-art Vision Transformer model, ViT-Large/16, achieves 91.15% and 83.54% accuracy on the PlantCLEF 2017 and ExpertLifeCLEF 2018 test sets, respectively; the best CNN model (ResNeSt-269e) error rate dropped by 22.91% and 28.34%. Apart from that, additional tricks increased the performance for the ViT-Base/32 by 3.72% on ExpertLifeCLEF 2018 and by 4.67% on PlantCLEF 2017. The retrieval approach achieved superior performance in all measured scenarios with accuracy margins of 0.28%, 4.13%, and 10.25% on ExpertLifeCLEF 2018, PlantCLEF 2017, and iNat2018-Plantae, respectively.
Rights:	© authors
Appears in Collections:	Články / Articles (NTIS) Články / Articles (KKY) OBD

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