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Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens
The control of plant leaf diseases is crucial as it affects the quality and production of plant species with an effect on the economy of any country. Automated identification and classification of plant leaf diseases is, therefore, essential for the reduction of economic losses and the conservation...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745985/ https://www.ncbi.nlm.nih.gov/pubmed/33332376 http://dx.doi.org/10.1371/journal.pone.0243243 |
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author | Khan, Asim Nawaz, Umair Ulhaq, Anwaar Robinson, Randall W. |
author_facet | Khan, Asim Nawaz, Umair Ulhaq, Anwaar Robinson, Randall W. |
author_sort | Khan, Asim |
collection | PubMed |
description | The control of plant leaf diseases is crucial as it affects the quality and production of plant species with an effect on the economy of any country. Automated identification and classification of plant leaf diseases is, therefore, essential for the reduction of economic losses and the conservation of specific species. Various Machine Learning (ML) models have previously been proposed to detect and identify plant leaf disease; however, they lack usability due to hardware sophistication, limited scalability and realistic use inefficiency. By implementing automatic detection and classification of leaf diseases in fruit trees (apple, grape, peach and strawberry) and vegetable plants (potato and tomato) through scalable transfer learning on Amazon Web Services (AWS) SageMaker and importing it into AWS DeepLens for real-time functional usability, our proposed DeepLens Classification and Detection Model (DCDM) addresses such limitations. Scalability and ubiquitous access to our approach is provided by cloud integration. Our experiments on an extensive image data set of healthy and unhealthy fruit trees and vegetable plant leaves showed 98.78% accuracy with a real-time diagnosis of diseases of plant leaves. To train DCDM deep learning model, we used forty thousand images and then evaluated it on ten thousand images. It takes an average of 0.349s to test an image for disease diagnosis and classification using AWS DeepLens, providing the consumer with disease information in less than a second. |
format | Online Article Text |
id | pubmed-7745985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77459852020-12-31 Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens Khan, Asim Nawaz, Umair Ulhaq, Anwaar Robinson, Randall W. PLoS One Research Article The control of plant leaf diseases is crucial as it affects the quality and production of plant species with an effect on the economy of any country. Automated identification and classification of plant leaf diseases is, therefore, essential for the reduction of economic losses and the conservation of specific species. Various Machine Learning (ML) models have previously been proposed to detect and identify plant leaf disease; however, they lack usability due to hardware sophistication, limited scalability and realistic use inefficiency. By implementing automatic detection and classification of leaf diseases in fruit trees (apple, grape, peach and strawberry) and vegetable plants (potato and tomato) through scalable transfer learning on Amazon Web Services (AWS) SageMaker and importing it into AWS DeepLens for real-time functional usability, our proposed DeepLens Classification and Detection Model (DCDM) addresses such limitations. Scalability and ubiquitous access to our approach is provided by cloud integration. Our experiments on an extensive image data set of healthy and unhealthy fruit trees and vegetable plant leaves showed 98.78% accuracy with a real-time diagnosis of diseases of plant leaves. To train DCDM deep learning model, we used forty thousand images and then evaluated it on ten thousand images. It takes an average of 0.349s to test an image for disease diagnosis and classification using AWS DeepLens, providing the consumer with disease information in less than a second. Public Library of Science 2020-12-17 /pmc/articles/PMC7745985/ /pubmed/33332376 http://dx.doi.org/10.1371/journal.pone.0243243 Text en © 2020 Khan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Khan, Asim Nawaz, Umair Ulhaq, Anwaar Robinson, Randall W. Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens |
title | Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens |
title_full | Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens |
title_fullStr | Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens |
title_full_unstemmed | Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens |
title_short | Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens |
title_sort | real-time plant health assessment via implementing cloud-based scalable transfer learning on aws deeplens |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745985/ https://www.ncbi.nlm.nih.gov/pubmed/33332376 http://dx.doi.org/10.1371/journal.pone.0243243 |
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