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A Deep Learning Approach for Detection and Localization of Leaf Anomalies

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Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators

Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 151))

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Abstract

The detection and localization of possible diseases in crops are usually automated by resorting to supervised deep learning approaches. In this work, we tackle these goals with unsupervised models, by applying three different types of autoencoders to a specific open-source dataset of healthy and unhealthy pepper and cherry leaf images. CAE, CVAE and VQ-VAE autoencoders are deployed to screen unlabeled images of such a dataset, and compared in terms of image reconstruction, anomaly removal, detection and localization. The vector-quantized variational architecture turns out to be the best performing one with respect to all these targets.

This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Notes

  1. 1.

    The image flip** around the vertical axis is also known as flop**.

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Acknowledgements

Massimiliano Lupo Pasini thanks Dr. Vladimir Protopopescu for his valuable feedback in the preparation of this manuscript. Massimiliano Lupo Pasini’s work was supported in part by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. Simona Perotto and Nicola Ferro acknowledge the support by MUR, grant Dipartimento di Eccellenza 2023–2027.

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Authors and Affiliations

  1. Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy

    Davide Calabrò

  2. Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA

    Massimiliano Lupo Pasini

  3. MOX - Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy

    Nicola Ferro & Simona Perotto

Authors
  1. Davide Calabrò
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  3. Nicola Ferro
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  4. Simona Perotto
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Correspondence to Davide Calabrò .

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Editors and Affiliations

  1. SISSA mathLab, Mathematics Area, International School for Advanced Studies SISSA, Trieste, Italy

    Gianluigi Rozza

  2. DiSPeA, University of Urbino, Urbino, Italy

    Giovanni Stabile

  3. Oden Institute, The University of Texas at Austin, Austin, TX, USA

    Max Gunzburger

  4. ICME, Stanford University, Stanford, CA, CA, USA

    Marta D'Elia

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Calabrò, D., Pasini, M.L., Ferro, N., Perotto, S. (2024). A Deep Learning Approach for Detection and Localization of Leaf Anomalies. In: Rozza, G., Stabile, G., Gunzburger, M., D'Elia, M. (eds) Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators. Lecture Notes in Computational Science and Engineering, vol 151. Springer, Cham. https://doi.org/10.1007/978-3-031-55060-7_3

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