Integrating blockchain and deep learning for intelligent greenhouse control and traceability

【Author】 Frikha, Tarek; Ktari, Jalel; Zalila, Bechir; Ghorbel, Oussama; Ben Amor, Nader

【Source】ALEXANDRIA ENGINEERING JOURNAL

【影响因子】6.626

【Abstract】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.

【Keywords】Smart agriculture; Deep Learning Vision; Raspberry Pi4; Blockchain

【发表时间】2023 15-Sep

【收录时间】2023-09-21

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-农牧领域