Integrating 5G and machine learning technologies for advanced PDM in smart farming.
In: Journal of Intelligent & Fuzzy Systems, Jg. 46 (2024-04-01), Heft 4, S. 9709-9726
academicJournal
Zugriff:
Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation. [ABSTRACT FROM AUTHOR]
Titel: |
Integrating 5G and machine learning technologies for advanced PDM in smart farming.
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Autor/in / Beteiligte Person: | Zhang, Weidong ; Tan, Huadi |
Zeitschrift: | Journal of Intelligent & Fuzzy Systems, Jg. 46 (2024-04-01), Heft 4, S. 9709-9726 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 1064-1246 (print) |
DOI: | 10.3233/JIFS-237482 |
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