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X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion
We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of Olea europaea L. infected by Xylella fastidiosa, named X-FIDO (Xylella FastIdiosa Detector for O. europaea L.). Previous work predicted disease from leaf images with deep learning but requ...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5641424/ https://www.ncbi.nlm.nih.gov/pubmed/29067037 http://dx.doi.org/10.3389/fpls.2017.01741 |
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author | Cruz, Albert C. Luvisi, Andrea De Bellis, Luigi Ampatzidis, Yiannis |
author_facet | Cruz, Albert C. Luvisi, Andrea De Bellis, Luigi Ampatzidis, Yiannis |
author_sort | Cruz, Albert C. |
collection | PubMed |
description | We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of Olea europaea L. infected by Xylella fastidiosa, named X-FIDO (Xylella FastIdiosa Detector for O. europaea L.). Previous work predicted disease from leaf images with deep learning but required a vast amount of data which was obtained via crowd sourcing such as the PlantVillage project. This approach has limited applicability when samples need to be tested with traditional methods (i.e., PCR) to avoid incorrect training input or for quarantine pests which manipulation is restricted. In this paper, we demonstrate that transfer learning can be leveraged when it is not possible to collect thousands of new leaf images. Transfer learning is the re-application of an already trained deep learner to a new problem. We present a novel algorithm for fusing data at different levels of abstraction to improve performance of the system. The algorithm discovers low-level features from raw data to automatically detect veins and colors that lead to symptomatic leaves. The experiment included images of 100 healthy leaves, 99 X. fastidiosa-positive leaves and 100 X. fastidiosa-negative leaves with symptoms related to other stress factors (i.e., abiotic factors such as water stress or others diseases). The program detects OQDS with a true positive rate of 98.60 ± 1.47% in testing, showing great potential for image analysis for this disease. Results were obtained with a convolutional neural network trained with the stochastic gradient descent method, and ten trials with a 75/25 split of training and testing data. This work shows potential for massive screening of plants with reduced diagnosis time and cost. |
format | Online Article Text |
id | pubmed-5641424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-56414242017-10-24 X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion Cruz, Albert C. Luvisi, Andrea De Bellis, Luigi Ampatzidis, Yiannis Front Plant Sci Plant Science We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of Olea europaea L. infected by Xylella fastidiosa, named X-FIDO (Xylella FastIdiosa Detector for O. europaea L.). Previous work predicted disease from leaf images with deep learning but required a vast amount of data which was obtained via crowd sourcing such as the PlantVillage project. This approach has limited applicability when samples need to be tested with traditional methods (i.e., PCR) to avoid incorrect training input or for quarantine pests which manipulation is restricted. In this paper, we demonstrate that transfer learning can be leveraged when it is not possible to collect thousands of new leaf images. Transfer learning is the re-application of an already trained deep learner to a new problem. We present a novel algorithm for fusing data at different levels of abstraction to improve performance of the system. The algorithm discovers low-level features from raw data to automatically detect veins and colors that lead to symptomatic leaves. The experiment included images of 100 healthy leaves, 99 X. fastidiosa-positive leaves and 100 X. fastidiosa-negative leaves with symptoms related to other stress factors (i.e., abiotic factors such as water stress or others diseases). The program detects OQDS with a true positive rate of 98.60 ± 1.47% in testing, showing great potential for image analysis for this disease. Results were obtained with a convolutional neural network trained with the stochastic gradient descent method, and ten trials with a 75/25 split of training and testing data. This work shows potential for massive screening of plants with reduced diagnosis time and cost. Frontiers Media S.A. 2017-10-10 /pmc/articles/PMC5641424/ /pubmed/29067037 http://dx.doi.org/10.3389/fpls.2017.01741 Text en Copyright © 2017 Cruz, Luvisi, De Bellis and Ampatzidis. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Cruz, Albert C. Luvisi, Andrea De Bellis, Luigi Ampatzidis, Yiannis X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion |
title | X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion |
title_full | X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion |
title_fullStr | X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion |
title_full_unstemmed | X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion |
title_short | X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion |
title_sort | x-fido: an effective application for detecting olive quick decline syndrome with deep learning and data fusion |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5641424/ https://www.ncbi.nlm.nih.gov/pubmed/29067037 http://dx.doi.org/10.3389/fpls.2017.01741 |
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