Smart Irrigation Along with AI-ML
Keywords:
Crop yield, Convolutional Neural Networks (CNNs), Internet of things (IoT), Sensors, Smart irrigation, Wireless Sensor Network (WSN)Abstract
Traditional agricultural methods have undergone a significant transformation due to the rapid development of embedded systems and Internet of Things (IoT) technologies, which have enabled the creation of more sustainable, intelligent, and efficient solutions. This literature review examines several significant research contributions related to precision agriculture and smart irrigation systems. The first study offers a low-cost and effective solution for small farms by implementing an Arduino-based smart irrigation system that automatically waters based on soil moisture levels. The second paper introduces a cloud-based smart agriculture model that shows scalability for large-scale operations by using IoT sensors to gather field data that is then processed in the cloud to inform agricultural decisions. The third study develops a precision farming system that reduces water waste and increases crop yield by using a Wireless Sensor Network (WSN) to monitor environmental parameters in real-time.
References
R. Nandhini, et al., “Arduino-based smart irrigation system using IoT,” Proceedings of the 3rd National Conference on Intelligent Information and Computing Technologies (IICT'17), 2017. Available: https://www.researchgate.net/profile/Anila-Satish/publication/321854296_
ARDUINO_BASED_SMART_IRRIGATION_SYSTEM_USING_IOT/links/5a3536090f7e9b10d8450893/ARDUINO-BASED-SMART-IRRIGATION-SYSTEM-USING-IOT.pdf
M. R. Suma and P. Madhumathy, “An optimal swift key generation and distribution for QKD,” Scientific and Technical Journal of Information Technologies, Mechanics and Optics, vol. 22, no. 1, pp. 101–113, Feb. 2022, doi: https://doi.org/10.17586/2226-1494-2022-22-1-101-113
P. Madhumathy and D. Sivakumar, “Reliable data gathering by Mobile Sink for wireless sensor networks,” International Conference on Communication and Signal Processing, Melmaruvathur, India, 2014, pp. 1348–1352, Apr. 2014, doi: https://doi.org/10.1109/iccsp.2014.6950069
M. S. Badar, S. Shamsi, M. Maksuf, and Adel Sharar Aldalbahi, “Applications of AI and ML in IoT,” CRC Press eBooks, pp. 273–290, Apr. 2021, doi: https://doi.org/10.1201/9781003107521-13
I. B., M. P., and N. Kavitha, “Brain tumor image segmentation and classification using SVM, CLAHE and ARKFCM,” Intelligent Decision Support Systems: Applications in Signal Processing, 2019, pp. 53–70. Available: https://www.degruyterbrill.com/document/doi/10.1515/
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M-n84GJ8ABe-bN
S. Banerjee and P. Madhumathy, “IoT-based health monitoring system for speech-impaired people using assistive wearable accelerometer,” Advanced Healthcare Systems: Empowering Physicians with IoT-Enabled Technologies, Wiley, Jan. 2022, pp. 81–99, doi: https://doi.org/10.1002/9781119769293.ch7
N. Kavitha, P. Madhumathy, R. M. Prasad, and D. N. Chandrappa, “Machine learning technique for breast cancer detection and classification,” Machine Learning for Computational Science and Engineering, vol. 1, no. 1, Apr. 2025, doi: https://doi.org/10.1007/s44379-025-00018-y
S. Roopashree, et al. “Machine learning approach: Enriching the knowledge of Ayurveda from Indian medicinal herbs.” Challenges and Applications of Data Analytics in Social Perspectives, edited by V. Sathiyamoorthi and Atilla Elçi, IGI Global Scientific Publishing, 2021, pp. 214–231. doi: https://doi.org/10.4018/978-1-7998-2566-1.ch012
I. Banerjee, “An agent cluster based routing protocol for enhancing lifetime of wireless sensor network,” 2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), pp. 265–268, Mar. 2019, doi: https://doi.org/10.1109/icatiece45860.2019.9063788
M. R. Suma, S. B. Kumar, “Analysis of sense amplifier circuits in nanometer technologies,” 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), pp. 1–4, Mar. 2017, doi: https://doi.org/10.1109/icscn.2017.8085666
P. Bhardwaj, A. Srivastava, A. K. Pandey, A. Singh, and B. Tripathi, “IoT-based smart agriculture aid system using Raspberry Pi,” International Journal of Engineering and Advanced Technology (IJEAT), vol. 10, no. 5, pp. 274–278, Jun. 2021, doi: https://doi.org/10.35940/ijeat.e2767.0610521
N. Kavitha and P. Madhumathy, “Development of an IoT-based atmospheric fine dust monitoring system,” Studies in Systems, Decision and Control, pp. 263–279, Jan. 2020, doi: https://doi.org/10.1007/978-3-030-39047-1_12
