USING IOT DEVICES TO MONITOR PLANT HEALTH IN AGRICULTURE
DOI:
https://doi.org/10.32689/maup.it.2025.1.15Keywords:
Internet of Things, IoT device, digital technologies, neural networks, agricultureAbstract
The article considers the process of developing an intelligent IoT device for monitoring plant health in agriculture. The system architecture, neural network training methodology, and real-time image analysis mechanism are described. The purpose of this study is to develop and experimentally verify an intelligent IoT device for monitoring plant health, capable of detecting signs of diseases and the presence of pests using convolutional neural networks. Research methodology. The study uses a comprehensive approach that combines hardware and software into a single IoT plant monitoring system. The YOLOv9 convolutional neural network is used to analyze plant images. The scientific novelty lies in the creation and testing of a prototype of an autonomous intelligent device for monitoring plant health. A system architecture is proposed that allows local detection of signs of diseases and pests without connecting to external servers, which reduces delays and increases reliability in field conditions. Conclusions. As a result of the research, a prototype of an intelligent IoT device for monitoring the condition of plants in agriculture was successfully developed and tested. The experiments conducted proved the effectiveness of the device in a real environment.
References
A review of deep learning techniques used in agriculture. Computers and Electronics in Agriculture. 2023. Vol. 209. Article 107877. DOI: 10.1016/j.compag.2023.107877.
Applications of Machine Learning and Deep Learning in Agriculture: A Comprehensive Review. Discover Artificial Intelligence. 2025. Vol. 3, No. 1. Article 33. DOI: 10.1007/s44196-025-00033-8.
Bosch Sensortec. BME280 Combined humidity and pressure sensor URL: https://www.bosch-sensortec.com/products/environmental-sensors/humidity-sensors-bme280/ Дата звернення: 21 квітня 2025 р.
Garg S., Pundir P., Jindal H., Saini H., Garg S. Towards a Multimodal System for Precision Agriculture using IoT and Machine Learning. 12th ICCCNT 2021 conference at IIT Kharagpur, India. 2021. https://doi.org/10.48550/arXiv.2107.04895.
Hammad Shahab, Muhammad Naeem, Muhammad Iqbal, Muhammad Aqeel, Syed Sajid Ullah. IoT-driven smart agricultural technology for real-time soil and crop optimization. Smart Agricultural Technology. Volume 10, March 2025, 100847. https://doi.org/10.1016/j.atech.2025.100847.
Natraj A. A., Lee B., Castiblanco F. A., Buckmaster D. R., Wang C. C., Love D. J., Krogmeier J. V., Butt M. M., Ghosh A. Ambient IoT: Communications Enabling Precision Agriculture. arXiv. 2024. URL: https://arxiv.org/abs/2409.12281.
Plant Disease Detection Using Deep Learning Techniques. Journal of Intelligent Agriculture and Precision. 2025. Vol. 1, No. 2. P. 55–64. DOI: 10.1234/jiap.2025.227089.
Prem Rajak, Abhratanu Ganguly, Satadal Adhikary, Suchandra Bhattacharya. Internet of Things and smart sensors in agriculture: Scopes and challenges. Journal of Agriculture and Food Research. Volume 14, December 2023, 100776. https://doi.org/10.1016/j.jafr.2023.100776.
Qin R., Wang Y., Liu X., Yu H. Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring. Frontiers in Plant Science, 2025. 15, 1485903.
Raspberry Pi Foundation. Raspberry Pi Zero 2 W. URL: https://www.raspberrypi.com/products/raspberry-pizero-2-w/. –Дата звернення: 21 квітня 2025 р.
Sadowski S., Spachos P. Wireless Technologies for Agricultural Monitoring using Internet of Things Devices with Energy Harvesting Capabilities. arXiv. 2020. URL: https://arxiv.org/abs/2005.02477.
Udutalapally V., Mohanty S. P., Pallagani V., Khandelwal V. sCrop: A Internet-of-Agro-Things (IoAT) Enabled Solar Powered Smart Device for Automatic Plant Disease Prediction. Published in arXiv.org 9 May 2020 DOI:10.48550/arXiv.2005.06342.
