To understand the current state of research and to also reveal the challenges and opportunities for future research in the field of internet of water things for water quality monitoring, in this study, we conduct a bibliometric analysis and a comprehensive review of the published research from 2012 to 2022 on internet of water things for water quality monitoring. The bibliometric analysis method was used to analyze the collected published papers from the Scopus database. This helped to determine the majority of research topics in the internet of water things for water quality monitoring research field. Subsequently, an in depth comprehensive review of the relevant literature was conducted to provide insight into recent advances in internet of water things for water quality monitoring, and to also determine the research gaps in the field. Based on the comprehensive review of literature, we identified that reviews of the research topic of resource management in internet of water things for water quality monitoring is less common. Hence, this study aimed to fill this research gap in the field of internet of water things for water quality monitoring. To address the resource management challenges associated with the internet of water things designed for water quality monitoring applications, this paper is focused on the use of game theory methods. Game theory methods are embedded with powerful mathematical techniques that may be used to model and analyze the behaviors of various individual, or any group, of water quality sensors. Additionally, various open research issues are pointed out as future research directions.

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A Bibliometric Analysis and Review of Resource Management in Internet of Water Things: The Use of Game Theory

Wheat is an important staple crop in the global food chain. The production of wheat in many regions is constrained by the lack of use of advanced technologies for wheat monitoring. Unmanned Aerial Vehicles (UAVs) is an important platform in remote sensing for providing near real-time farm-scale information. This information aids in making recommendations for monitoring and improving crop management to ensure food security. This study appraised global scientific research trends on wheat and UAV studies between 2005 and 2021, using a bibliometric method. The 398 published documents were mined from Web of Science, Scopus, and Dimensions. Results showed that an annual growth rate of 23.94% indicates an increase of global research based on wheat and UAVs for the surveyed period. The results revealed that China and USA were ranked as the top most productive countries, and thus their dominance in UAVs extensive usage and research developments for wheat monitoring during the study period. Additionally, results showed a low countries research collaboration prevalent trend, with only China and Australia managing multiple country publications. Thus, most of the wheat- and UAV-related studies were based on intra-country publications. Moreover, the results showed top publishing journals, top cited documents, Zipf’s law authors keywords co-occurrence network, thematic evolution, and spatial distribution map with the lack of research outputs from Southern Hemisphere. The findings of also show that “UAV” is fundamental in all keywords with the largest significant appearance in the field. This connotes that UAV efficiency was important for most studies that were monitoring wheat and provided vital information on spatiotemporal changes and variability for crop management. Findings from this study may be useful in policy-making decisions related to the adoption and subsidizing of UAV operations for different crop management strategies designed to enhance crop yield and the direction of future studies.

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Global Research Trends for Unmanned Aerial Vehicle Remote Sensing Application in Wheat Crop Monitoring

A web-based GIS platform supporting innovative irrigation management techniques at farm-scale for the Mediterranean island of Crete.

Almost fifty years have passed since the idea to retrieve a value for Evapotranspiration (ET) using remote sensing techniques was first considered. Numerous ET models have been proposed, validated and improved along these five decades, as the satellites and sensors onboard were enhanced. This study reviews most of the efforts in the progress towards providing a trustworthy value of ET by means of thermal remote sensing data. It starts with an in-depth reflection of the surface energy balance concept and of each of its terms, followed by the description of the approaches taken by remote sensing models to estimate ET from it in the last thirty years. This work also includes a chronological review of the modifications suggested by several researchers, as well as representative validations studies of such ET models. Present limitations of ET estimated with remote sensors onboard orbiting satellites, as well as at surface level, are raised. Current trends to face such limitations and a future perspective of the discipline are also exposed, for the reader’s inspiration.

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Evapotranspiration Acquired with Remote Sensing Thermal-Based Algorithms: A State-of-the-Art Review

In accordance with the rapid proliferation of machine learning (ML) and data management, ML applications have evolved to encompass all engineering disciplines. Owing to the importance of the world’s water supply throughout the rest of this century, much research has been concentrated on the application of ML strategies to integrated water resources management (WRM). Thus, a thorough and well-organized review of that research is required. To accommodate the underlying knowledge and interests of both artificial intelligence (AI) and the unresolved issues of ML in WRM, this overview divides the core fundamentals, major applications, and ongoing issues into two sections. First, the basic applications of ML are categorized into three main groups, prediction, clustering, and reinforcement learning. Moreover, the literature is organized in each field according to new perspectives, and research patterns are indicated so attention can be directed toward where the field is headed. In the second part, the less investigated field of WRM is addressed to provide grounds for future studies. The widespread applications of ML tools are projected to accelerate the formation of sustainable WRM plans over the next decade.

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Application of Machine Learning in Water Resources Management: A Systematic Literature Review

Agriculture consumes an important ratio of the water reserve in irrigated areas. The improvement of irrigation is becoming essential to reduce this high water consumption by adapting supplies to the crop needs and avoiding losses. This global issue has prompted many scientists to reflect on sustainable solutions using innovative technologies, namely Unmanned Aerial Vehicles (UAV), Machine Learning (ML), and the Internet of Things (IoT). This article aims to present an overview of the use of these new technologies in the analysis of the water status of crops for better irrigation management, with an emphasis on arboriculture. The review demonstrated the importance of UAV-ML-IoT technologies. This contribution is due to the relevant information that can be collected from IoT sensors and extracted from UAV images through various sensors (RGB, multispectral, hyperspectral, thermal), and the ability of ML models to monitor and predict water status. The review in this paper is organized into four main sections: the use of UAV in arboriculture, UAV for irrigation management in arboriculture, IoT systems and irrigation management, and ML for data processing and decision-making. A discussion is presented regarding the prospects for smart irrigation using geospatial technologies and machine learning.

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Towards Smart Irrigation: A Literature Review on the Use of Geospatial Technologies and Machine Learning in the Management of Water Resources in Arboriculture

Youssef Ahansal

Papers

Towards Smart Irrigation: A Literature Review on the Use of Geospatial Technologies and Machine Learning in the Management of Water Resources in Arboriculture