ReviewSensors (Basel, Switzerland)2024
Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Cyber-Physical System Integration of IoT Sensing and Machine Learning: A Cross-Domain Review of Decision Support and Control in Smart Buildings and Precision Agriculture.Sensors (Basel, Switzerland) · 2026Review
- AI and IoT in Sugar Beet Systems: A Review of Monitoring, VOC Sensing, and Post-Harvest Applications.Sensors (Basel, Switzerland) · 2026Review
- Cognitive integration of internet of things and feedforward learning models for smart irrigation in sustainable agriculture.Scientific reports · 2026Article
- Improved Weighted Quantum Whale Optimization with vision transformer for intrusion detection, atmospheric monitoring and recommendation in smart agriculture.Scientific reports · 2025Article
- Multi-Sensor Monitoring, Intelligent Control, and Data Processing for Smart Greenhouse Environment Management.Sensors (Basel, Switzerland) · 2025Review
- IoT Sensing for Advanced Irrigation Management: A Systematic Review of Trends, Challenges, and Future Prospects.Sensors (Basel, Switzerland) · 2025Review
- The Effects of AI-Supported Autonomous Irrigation Systems on Water Efficiency and Plant Quality: A Case Study ofPlants (Basel, Switzerland) · 2025Article
- Development of a Software and Hardware Complex for Monitoring Processes in Production Systems.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.