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Inside out: transforming images of lab-grown plants for machine learning applications in agriculture
INTRODUCTION: Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358354/ https://www.ncbi.nlm.nih.gov/pubmed/37483870 http://dx.doi.org/10.3389/frai.2023.1200977 |
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author | Krosney, Alexander E. Sotoodeh, Parsa Henry, Christopher J. Beck, Michael A. Bidinosti, Christopher P. |
author_facet | Krosney, Alexander E. Sotoodeh, Parsa Henry, Christopher J. Beck, Michael A. Bidinosti, Christopher P. |
author_sort | Krosney, Alexander E. |
collection | PubMed |
description | INTRODUCTION: Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of different growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available. METHODS: In this paper, we employ a contrastive unpaired translation (CUT) generative adversarial network (GAN) and simple image processing techniques to translate indoor plant images to appear as field images. While we train our network to translate an image containing only a single plant, we show that our method is easily extendable to produce multiple-plant field images. RESULTS: Furthermore, we use our synthetic multi-plant images to train several YoloV5 nano object detection models to perform the task of plant detection and measure the accuracy of the model on real field data images. DISCUSSION: The inclusion of training data generated by the CUT-GAN leads to better plant detection performance compared to a network trained solely on real data. |
format | Online Article Text |
id | pubmed-10358354 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103583542023-07-21 Inside out: transforming images of lab-grown plants for machine learning applications in agriculture Krosney, Alexander E. Sotoodeh, Parsa Henry, Christopher J. Beck, Michael A. Bidinosti, Christopher P. Front Artif Intell Artificial Intelligence INTRODUCTION: Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of different growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available. METHODS: In this paper, we employ a contrastive unpaired translation (CUT) generative adversarial network (GAN) and simple image processing techniques to translate indoor plant images to appear as field images. While we train our network to translate an image containing only a single plant, we show that our method is easily extendable to produce multiple-plant field images. RESULTS: Furthermore, we use our synthetic multi-plant images to train several YoloV5 nano object detection models to perform the task of plant detection and measure the accuracy of the model on real field data images. DISCUSSION: The inclusion of training data generated by the CUT-GAN leads to better plant detection performance compared to a network trained solely on real data. Frontiers Media S.A. 2023-07-06 /pmc/articles/PMC10358354/ /pubmed/37483870 http://dx.doi.org/10.3389/frai.2023.1200977 Text en Copyright © 2023 Krosney, Sotoodeh, Henry, Beck and Bidinosti. https://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) and the copyright owner(s) 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 | Artificial Intelligence Krosney, Alexander E. Sotoodeh, Parsa Henry, Christopher J. Beck, Michael A. Bidinosti, Christopher P. Inside out: transforming images of lab-grown plants for machine learning applications in agriculture |
title | Inside out: transforming images of lab-grown plants for machine learning applications in agriculture |
title_full | Inside out: transforming images of lab-grown plants for machine learning applications in agriculture |
title_fullStr | Inside out: transforming images of lab-grown plants for machine learning applications in agriculture |
title_full_unstemmed | Inside out: transforming images of lab-grown plants for machine learning applications in agriculture |
title_short | Inside out: transforming images of lab-grown plants for machine learning applications in agriculture |
title_sort | inside out: transforming images of lab-grown plants for machine learning applications in agriculture |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358354/ https://www.ncbi.nlm.nih.gov/pubmed/37483870 http://dx.doi.org/10.3389/frai.2023.1200977 |
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