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crossrefSensors2024-11-23Cited by 58

Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review

Bryan Nsoh, Abia Katimbo, Hongzhi Guo, Derek M. Heeren, Hope Njuki Nakabuye, Xin Qiao, Yufeng Ge, Daran R. Rudnick, Joshua Wanyama, Erion Bwambale, Shafik Kiraga

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.

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crossrefSensors2023-12-31Cited by 36

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Defect detection is a key element of quality control in today’s industries, and the process requires the incorporation of automated methods, including image sensors, to detect any potential defects that may occur during the manufacturing process. While there are various methods t…

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crossrefSensors2024-01-29Cited by 24

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The emerging yet promising paradigm of the Internet of Vehicles (IoV) has recently gained considerable attention from researchers from academia and industry. As an indispensable constituent of the futuristic smart cities, the underlying essence of the IoV is to facilitate vehicle…

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crossrefSensors2025-05-31Cited by 2

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This study presents a novel artificial intelligence-driven architecture for real-time sensor fusion in autonomous vehicles (AVs), leveraging Apache Kafka and MongoDB for synchronous and asynchronous data processing to enhance resilience against sensor failures and dynamic conditi…

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crossrefSensors2024-08-09Cited by 13

Roadmap of Adversarial Machine Learning in Internet of Things-Enabled Security Systems

Yasmine Harbi, Khedidja Medani, Chirihane Gherbi, Zibouda Aliouat, Saad Harous

Machine learning (ML) represents one of the main pillars of the current digital era, specifically in modern real-world applications. The Internet of Things (IoT) technology is foundational in developing advanced intelligent systems. The convergence of ML and IoT drives significan…

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crossrefSensors2023-10-20Cited by 2

A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories

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Automated Guided Vehicles (AGVs) are becoming popular at many manufacturing facilities. To ensure mobility and flexibility, AGVs are often controlled by wireless communication, eliminating the constraints of physical cables. These AGVs require multiple Access Points (APs) to ensu…

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crossrefSensors2023-07-17Cited by 97

Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety

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Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has been proven to be one of the m…

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