Integrating blockchain and deep learning for intelligent greenhouse control and traceability
This research presents a solution that combines deep learning-based image processing, blockchain technology, and the Internet of Things (IoT) to achieve smarter control and traceability in greenhouse operations within the agricultural sector. By integrating these technologies, the aim is to overcome...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823007081 |
| _version_ | 1797686875831402496 |
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| author | Tarek Frikha Jalel Ktari Bechir Zalila Oussama Ghorbel Nader Ben Amor |
| author_facet | Tarek Frikha Jalel Ktari Bechir Zalila Oussama Ghorbel Nader Ben Amor |
| author_sort | Tarek Frikha |
| collection | DOAJ |
| description | This research presents a solution that combines deep learning-based image processing, blockchain technology, and the Internet of Things (IoT) to achieve smarter control and traceability in greenhouse operations within the agricultural sector. By integrating these technologies, the aim is to overcome challenges posed by climate change, plant growth, limited agricultural land, and water scarcity, while enhancing crop yields and ensuring efficient and secure operations. The proposed system automates image capture, measurement, storage, and monitoring of environmental parameters in greenhouses, utilizing highly accurate image processing techniques with a 98% success rate. The integration of blockchain technology establishes an immutable and transparent record of transactions and data points, thereby improving traceability across the agricultural supply chain. This comprehensive approach fosters accountability, transparency, and trust, empowering consumers to make well-informed decisions regarding the products they purchase. Ultimately, this research contributes to advancing efficient and sustainable agricultural practices. |
| first_indexed | 2024-03-12T01:10:56Z |
| format | Article |
| id | doaj.art-953d957ad1cd43b1881fa3f004a9f1c9 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-12T01:10:56Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-953d957ad1cd43b1881fa3f004a9f1c92023-09-14T04:53:14ZengElsevierAlexandria Engineering Journal1110-01682023-09-0179259273Integrating blockchain and deep learning for intelligent greenhouse control and traceabilityTarek Frikha0Jalel Ktari1Bechir Zalila2Oussama Ghorbel3Nader Ben Amor4Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia; ENIS, University of Sfax, Tunisia; Corresponding author.CES Lab, ENIS, University of Sfax, Sfax, TunisiaReDCAD, ENIS, University of Sfax, Sfax, TunisiaCES Lab, ENIS, University of Sfax, Sfax, TunisiaCES Lab, ENIS, University of Sfax, Sfax, TunisiaThis research presents a solution that combines deep learning-based image processing, blockchain technology, and the Internet of Things (IoT) to achieve smarter control and traceability in greenhouse operations within the agricultural sector. By integrating these technologies, the aim is to overcome challenges posed by climate change, plant growth, limited agricultural land, and water scarcity, while enhancing crop yields and ensuring efficient and secure operations. The proposed system automates image capture, measurement, storage, and monitoring of environmental parameters in greenhouses, utilizing highly accurate image processing techniques with a 98% success rate. The integration of blockchain technology establishes an immutable and transparent record of transactions and data points, thereby improving traceability across the agricultural supply chain. This comprehensive approach fosters accountability, transparency, and trust, empowering consumers to make well-informed decisions regarding the products they purchase. Ultimately, this research contributes to advancing efficient and sustainable agricultural practices.http://www.sciencedirect.com/science/article/pii/S1110016823007081Smart agricultureDeep Learning Vision, Raspberry Pi4Blockchain |
| spellingShingle | Tarek Frikha Jalel Ktari Bechir Zalila Oussama Ghorbel Nader Ben Amor Integrating blockchain and deep learning for intelligent greenhouse control and traceability Alexandria Engineering Journal Smart agriculture Deep Learning Vision, Raspberry Pi4 Blockchain |
| title | Integrating blockchain and deep learning for intelligent greenhouse control and traceability |
| title_full | Integrating blockchain and deep learning for intelligent greenhouse control and traceability |
| title_fullStr | Integrating blockchain and deep learning for intelligent greenhouse control and traceability |
| title_full_unstemmed | Integrating blockchain and deep learning for intelligent greenhouse control and traceability |
| title_short | Integrating blockchain and deep learning for intelligent greenhouse control and traceability |
| title_sort | integrating blockchain and deep learning for intelligent greenhouse control and traceability |
| topic | Smart agriculture Deep Learning Vision, Raspberry Pi4 Blockchain |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823007081 |
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