Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications
Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and su...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/14/2/341 |
| _version_ | 1827344422054920192 |
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| author | Claudia Leslie Arellano Vidal Joseph Edward Govan |
| author_facet | Claudia Leslie Arellano Vidal Joseph Edward Govan |
| author_sort | Claudia Leslie Arellano Vidal |
| collection | DOAJ |
| description | Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector. |
| first_indexed | 2024-03-07T22:46:02Z |
| format | Article |
| id | doaj.art-d60c6bb79e5b4f40b1da356b25e39f9c |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-07T22:46:02Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-d60c6bb79e5b4f40b1da356b25e39f9c2024-02-23T15:04:14ZengMDPI AGAgronomy2073-43952024-02-0114234110.3390/agronomy14020341Machine Learning Techniques for Improving Nanosensors in Agroenvironmental ApplicationsClaudia Leslie Arellano Vidal0Joseph Edward Govan1School of Business, Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Peñalolén, Santiago 7550344, ChileFacultad de Ciencias Agronomicas, Universidad de Chile, Santa Rosa 11315, La Pintana, Santiago 8820808, ChileNanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector.https://www.mdpi.com/2073-4395/14/2/341machine learningnanotechnologyagriculture |
| spellingShingle | Claudia Leslie Arellano Vidal Joseph Edward Govan Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications Agronomy machine learning nanotechnology agriculture |
| title | Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications |
| title_full | Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications |
| title_fullStr | Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications |
| title_full_unstemmed | Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications |
| title_short | Machine Learning Techniques for Improving Nanosensors in Agroenvironmental Applications |
| title_sort | machine learning techniques for improving nanosensors in agroenvironmental applications |
| topic | machine learning nanotechnology agriculture |
| url | https://www.mdpi.com/2073-4395/14/2/341 |
| work_keys_str_mv | AT claudialesliearellanovidal machinelearningtechniquesforimprovingnanosensorsinagroenvironmentalapplications AT josephedwardgovan machinelearningtechniquesforimprovingnanosensorsinagroenvironmentalapplications |