Integrating Satellite Imagery and Infield Sensors for Daily Spatial Plant Evapotranspiration Prediction: A Machine Learning-Driven Approach

Shimim, Farshina Nazrul; Glenn, Ethan M.; Felegari, Shilan; Griesbaum, Brett; Fike, John; Whitaker, Bradley M.; Nugent, Paul W.

doi:10.1109/ietc61393.2024.10564271

2024 Intermountain Engineering, Technology and Computing (IETC) 2024

DOI: 10.1109/ietc61393.2024.10564271

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Integrating Satellite Imagery and Infield Sensors for Daily Spatial Plant Evapotranspiration Prediction: A Machine Learning-Driven Approach

Farshina Nazrul Shimim,

Ethan M. Glenn,

Shilan Felegari

et al.

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Cited by 1 publication

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

Nsoh,

Katimbo,

Guo

et al. 2024

Sensors

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

Nsoh,

Katimbo,

Guo

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Integrating Satellite Imagery and Infield Sensors for Daily Spatial Plant Evapotranspiration Prediction: A Machine Learning-Driven Approach

Cited by 1 publication

References 35 publications

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

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

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