The utilization of IoT-based humidity monitoring method and convolutional neural networks for orchid seed germination

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The utilization of IoT-based humidity monitoring method and convolutional neural networks for orchid seed germination

Muhammad Ridhan FIRDAUS, Hilal H. NUHA, Mohamed MOHANDES, Agus HAERUMAN

Abstract. This research endeavors to advance orchid seed germination efficiency through the development of an Internet of Things (IoT)-based humidity monitoring system integrated with Convolutional Neural Networks (CNN). Recognizing the pivotal role of proper humidity in orchid seed sowing, the proposed system employs humidity sensors connected to an IoT platform for real-time data collection. The collected data undergoes analysis and prediction by CNN, elucidated through graphical representations such as histograms, line charts, and scatterplot charts. By synergizing IoT technology with artificial intelligence, this innovative system contributes positively to orchid seed sowing efficiency, empowering farmers and orchid cultivators to optimize plant growth conditions. Furthermore, the adaptability of this approach extends beyond orchids, making it applicable to various crop seeding applications through parameter modifications tailored to specific needs.

Keywords
CNN, IOT, Orchid Seeding

Published online 7/15/2024, 8 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Muhammad Ridhan FIRDAUS, Hilal H. NUHA, Mohamed MOHANDES, Agus HAERUMAN, The utilization of IoT-based humidity monitoring method and convolutional neural networks for orchid seed germination, Materials Research Proceedings, Vol. 43, pp 261-268, 2024

DOI: https://doi.org/10.21741/9781644903216-34

The article was published as article 34 of the book Renewable Energy: Generation and Application

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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