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An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically b...

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Autores principales: Beck, Michael A., Liu, Chen-Yi, Bidinosti, Christopher P., Henry, Christopher J., Godee, Cara M., Ajmani, Manisha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745972/
https://www.ncbi.nlm.nih.gov/pubmed/33332382
http://dx.doi.org/10.1371/journal.pone.0243923
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author Beck, Michael A.
Liu, Chen-Yi
Bidinosti, Christopher P.
Henry, Christopher J.
Godee, Cara M.
Ajmani, Manisha
author_facet Beck, Michael A.
Liu, Chen-Yi
Bidinosti, Christopher P.
Henry, Christopher J.
Godee, Cara M.
Ajmani, Manisha
author_sort Beck, Michael A.
collection PubMed
description A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and image segementation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.
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spelling pubmed-77459722020-12-31 An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture Beck, Michael A. Liu, Chen-Yi Bidinosti, Christopher P. Henry, Christopher J. Godee, Cara M. Ajmani, Manisha PLoS One Research Article A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and image segementation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector. Public Library of Science 2020-12-17 /pmc/articles/PMC7745972/ /pubmed/33332382 http://dx.doi.org/10.1371/journal.pone.0243923 Text en © 2020 Beck 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
Beck, Michael A.
Liu, Chen-Yi
Bidinosti, Christopher P.
Henry, Christopher J.
Godee, Cara M.
Ajmani, Manisha
An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
title An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
title_full An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
title_fullStr An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
title_full_unstemmed An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
title_short An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
title_sort embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745972/
https://www.ncbi.nlm.nih.gov/pubmed/33332382
http://dx.doi.org/10.1371/journal.pone.0243923
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