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

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 realtime 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.