Biological Systems Engineering, Department of
Document Type
Article
Date of this Version
11-8-2024
Citation
Nsoh, B.; Katimbo, A.; Guo, H.; Heeren, D.M.; Nakabuye, H.N.; Qiao, X.; Ge, Y.; Rudnick, D.R.; Wanyama, J.; Bwambale, E.; et al. Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review. Sensors 2024, 24, 7480. https://doi.org/10.3390/ s24237480
Abstract
This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this review, the automation of each component is examined in the irrigation management pipeline from data collection to application while analyzing its effectiveness, efficiency, and integration with various precision agriculture technologies. It also investigates the role of the interoperability, standardization, and cybersecurity of IoT-based automated solutions for irrigation applications. Furthermore, in this review, the existing gaps are identified and solutions are proposed for seamless integration across multiple sensor suites for automated systems, aiming to achieve fully autonomous and scalable irrigation management. The findings highlight the transformative potential of automated irrigation systems to address global food challenges by optimizing water use and maximizing crop yields.
Included in
Bioresource and Agricultural Engineering Commons, Environmental Engineering Commons, Other Civil and Environmental Engineering Commons
Comments
Open access.