[2210.03558] A deep learning approach for detection and localization of leaf anomalies
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2210.03558 (cs)
[Submitted on 7 Oct 2022]

Title:A deep learning approach for detection and localization of leaf anomalies

Authors:Davide Calabrò, Massimiliano Lupo Pasini, Nicola Ferro, Simona Perotto
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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.
Comments: 23 pages, 8 figures
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML)
MSC classes: 68T10, 68T45, 68U10
ACM classes: I.2.5; I.2.6; I.2.10; I.3.6; I.3.8; I.4.2; I.4.5; I.4.9; I.5.0; I.5.4; J.2; J.3
Cite as: arXiv:2210.03558 [cs.CV]
  (or arXiv:2210.03558v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2210.03558

Submission history

From: Massimiliano Lupo Pasini Dr. [view email]
[v1] Fri, 7 Oct 2022 13:45:18 UTC (4,618 KB)
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