Development of an IoT Based Irrigation Control System using Convolutional Neural Network
Abstract
The automation of irrigation activities has the potential to revolutionize traditional manual and static irrigation practices, leading to increased productivity with reduced human intervention. Manual irrigation practices often result in water wastage or inadequate water supply to specific crops, as different crops have varying water requirements (crop water need). Moreover, manual irrigation methods consume significant time and effort, especially when the farmland is located at a distance. This paper presents an IoT-based irrigation system that utilizes computer vision technology to capture and recognize crops in the irrigation field using a Convolutional Neural Network (CNN) model. The developed system continuously monitors and maintains the optimal soil moisture content for each specific crop, employing soil moisture and temperature sensors. The control unit of the system is implemented using the Raspberry Pi 3b+ platform. The performance of the developed system was evaluated using two key metrics: Accuracy and Response time. The CNN model achieved high accuracy, with a stabilized accuracy of 95 percent after 50 epochs of training and validation, using a dataset of 800 pictures. This indicates the system's capability to accurately identify crops in the field. The response time of the system was assessed through ten trials, resulting in an average response time of 14.3 seconds, which is considered satisfactory. The findings of this study demonstrate the effectiveness of the proposed IoT-based irrigation system in automating irrigation processes and optimizing water usage. By integrating crop recognition, soil moisture monitoring, and temperature sensing, the system ensures efficient irrigation practices, reducing water wastage and minimizing human effort. The successful implementation of the developed system paves the way for intelligent and dynamic irrigation systems, fostering higher agricultural productivity and sustainable water resource management.
Full Text:
PDFReferences
Abd Rahman, M. K. I., Abidin, M. S. Z., Mahmud, M. S. A., Buyamin, S., Ishak, M. H. I., & Emmanuel, A. A. (2019). Advancement of a smart fibrous capillary irrigation management system with an internet of things integration. Bulletin of Electrical Engineering and Informatics, 8(4), 1402–1410. https://doi.org/10.11591/eei.v8i4.1606
Abubakre, O. R., Ubadike, O., Aibinu, A. M., & Abiodun, T. (2021). Artificial Neural Network Embedded Optimal Mobile Communication System. xx(x), 1–10.
Balaji, V. R. (2019). Smart irrigation system using Iot and image processing. International Journal of Engineering and Advanced Technology, 8(6 Special issue), 115–120. https://doi.org/10.35940/ijeat.F1024.0886S19
Chinedu, P. U., Nwankwo, W., & Aliu, D. (2020). Cloud Security Concerns : Assessing the Fears of Service Adoption. 1(December), 164–174.
CNN Image Classification | Image Classification Using CNN. (n.d.).
Cost, P. (2020). IOT Based Smart Agriculture Monitoring System. International Journal of Innovative Technology and Exploring Engineering, 9(9), 325–328. https://doi.org/10.35940/ijitee.i7142.079920
Daniel, A., & Momoh, M. O. (2021). A Computer Security System for Cloud Computing Based on Encryption Technique. Computer Engineering and Applications Journal, 10(1), 41–54.
DC Powered Pumps Selection Guide: Types, Features, Applications | Engineering360. (n.d.).
Dokhande, A., Bomble, C., Patil, R., Khandekar, P., Dhone, N., & Gode, P. C. (2019). A Review Paper on IoT Based Smart Irrigation System. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 5(1), 191–196.
Momoh, M. (2018). LabVIEW Interfaced PC-Based Fishpond Monitoring System. July. https://doi.org/10.13140/RG.2.2.23102.18243
Pawar, S. B., Rajput, P., & Shaikh, A. (2018). Smart Irrigation System Using IOT And Raspberry Pi. International Research Journal of Engineering and Technology (IRJE T), 05(08), 4.
Rouse, M. (2009). What is IoT (Internet of Things) and How Does It Work? In Cyber Resilience of Systems and Networks (pp. 1–150).
Sahu, T., & Verma, A. (2017). Automated Smart Irrigation System using Raspberry Pi. International Journal of Computer Applications, 172(6), 9–14. https://doi.org/10.5120/ijca2017915160
Sen, D., Dey, M., Kumar, S., & Boopathi, C. S. (2020). Smart Irrigation Using IoT. International Journal of Advanced Science and Technology, 29(4), 3080–3090.
Singh, K., Jain, S., Andhra, V., & Sharma, S. (2019). IoT based approach for smart irrigation system suited to multiple crop cultivation. International Journal of Engineering Research and Technology, 12(3), 357–363.
Sumit, S. (2018). A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way | by Sumit Saha | Towards Data Science. Towards Data Science.
Wang, P., Valerdi, R., Zhou, S., & Li, L. (2015). Introduction : Advances in IoT research and applications. March, 239–241. https://doi.org/10.1007/s10796-015-9549-2
What is a Raspberry Pi? | Opensource.com. (n.d.).
Yasin, H. M., Zeebaree, S. R. M., & Zebari, I. M. I. (2019). Arduino Based Automatic Irrigation System: Monitoring and SMS Controlling. 4th Scientific International Conference Najaf, SICN 2019, 3(1), 109–114. https://doi.org/10.1109/SICN47020.2019.9019370
Refbacks
- There are currently no refbacks.