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Design and implementation of crop monitoring and irrigation system using IoT and cloud computing
Author(s)
Bello, Adamou
Date Issued
2024
Type
Thesis
Publisher
Cape Peninsula University of Technology
Abstract
Farmers are under great strain due to the world's expanding population and increased awareness of the environmental demands that agriculture makes on the environment. They must increase yields to feed more people while also ensuring that their techniques are sustainable. A balance between intense production and environmental care is required, which cannot be accomplished within the limitations of traditional farming. For this reason, many are turning to The Internet of Things (IoT).
The integration of IoT and Cloud Computing, facilitated by Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN) technologies, has emerged as a transformative force in the field of agriculture. This research explores designing, developing, and validating a Crop Monitoring and Irrigation System that seamlessly integrates IoT devices, cloud computing capabilities, and user-friendly interfaces. The system empowers farmers with real-time insights into soil conditions, crop health, and personalised recommendations for irrigation.
A comprehensive methodology was employed to achieve this, incorporating various engineering tools and software. The development and testing processes utilised Tera Term for terminal emulation, STM32CubeIDE 1.13.1 and STM32CubeMX for microcontroller configuration and programming, and draw.io for system design and documentation. PyCharm Community Edition 2023.2.1 was used for Python development, while STM32CubeMonitor facilitated real-time data monitoring. Postman API Platform was employed for API testing, and Node-Red was used for flow-based development. Both Python and C programming languages were integral to the system’s development.
Through rigorous testing and validation, the operational success of the system is confirmed, ensuring reliable data availability on both the Loriot platform and user applications. The study contributes to the advancement of smart agriculture and presents implications for scientific research, providing a valuable resource for the study of crop characteristics and regional phenomena. Despite certain limitations, the system showcases potential for continuous innovation and adaptation to evolving agricultural needs.
The integration of IoT and Cloud Computing, facilitated by Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN) technologies, has emerged as a transformative force in the field of agriculture. This research explores designing, developing, and validating a Crop Monitoring and Irrigation System that seamlessly integrates IoT devices, cloud computing capabilities, and user-friendly interfaces. The system empowers farmers with real-time insights into soil conditions, crop health, and personalised recommendations for irrigation.
A comprehensive methodology was employed to achieve this, incorporating various engineering tools and software. The development and testing processes utilised Tera Term for terminal emulation, STM32CubeIDE 1.13.1 and STM32CubeMX for microcontroller configuration and programming, and draw.io for system design and documentation. PyCharm Community Edition 2023.2.1 was used for Python development, while STM32CubeMonitor facilitated real-time data monitoring. Postman API Platform was employed for API testing, and Node-Red was used for flow-based development. Both Python and C programming languages were integral to the system’s development.
Through rigorous testing and validation, the operational success of the system is confirmed, ensuring reliable data availability on both the Loriot platform and user applications. The study contributes to the advancement of smart agriculture and presents implications for scientific research, providing a valuable resource for the study of crop characteristics and regional phenomena. Despite certain limitations, the system showcases potential for continuous innovation and adaptation to evolving agricultural needs.
Additional information
Thesis (MEng (Electrical Engineering))--Cape Peninsula University of Technology, 2024
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Bello, A_199023824.pdf
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2.98 MB
Format
Adobe PDF
Checksum
(MD5):06c8eb133a000b6e908bd99a74ef82c1