Smart drip irrigation systems using IoT: a review of architectures, machine learning models, and emerging trends

The Internet of Things (IoT) integration in drip irrigation systems is examined in this review paper, with an emphasis on automation, data-driven optimization, and real-time monitoring. The integration of IoT-driven automated drip irrigation represents a transformative leap toward sustainable agriculture, optimizing water conservation, crop productivity, and environmental resilience. Precision farming technologies like artificial intelligence (AI), the Internet of Things (IoT), and 5G-enabled automation are essential for improving irrigation efficiency through real- time monitoring, predictive analytics, and resource optimization as the challenges of climate change and food security worsen. Case studies, including Microsoft’s AI-driven initiative in Andhra Pradesh, have demonstrated significant improvements, such as a 30% increase in yield and up to 70% water savings through IoT-based systems. Machine learning techniques like KNN, SVM, ANN, and RF have further refined irrigation scheduling, achieving over 98% accuracy. This review analyzed 56 peer-reviewed studies published between 2015 and 2024, selected based on relevance to IoT-enabled drip irrigation with particular emphasis on literature from the last three years (2022–2024) with AI/ML integration, and deployment aspects. The key research gap identified is the absence of standardized benchmarks, open datasets, and scalable frameworks, which restrict the transition from pilot-scale studies to large-scale real-world adoption. Despite the advantages, barriers such as high initial investment, connectivity limitations, cybersecure- risks, and technical complexity hinder widespread adoption. Addressing these challenges through cost-effective solutions, policy interventions, subsidies, and farmer training programs will be crucial. The evolution toward Industry 5.0, emphasizing human–machine collaboration, AI-powered AgroBots, and energy-efficient smart irrigation solutions, underscores the future of precision agriculture. Highlights.

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Publication Details

Journal
Discover Agriculture
Published
2025-11-19
DOI
https://doi.org/10.1007/s44279-025-00430-1
Citations
4
Primary Topic
Irrigation Practices and Water Management
Type
article
Field-Weighted Citation Impact
4.74
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article

Smart drip irrigation systems using IoT: a review of architectures, machine learning models, and emerging trends

Chitra Shukla, T. Vijay Kumar, Nikita Jaiswal
4 citations
Discover Agriculture
Irrigation Practices and Water Management
4.74
article

Smart drip irrigation systems using IoT: a review of architectures, machine learning models, and emerging trends

Chitra Shukla, T. Vijay Kumar, Nikita Jaiswal
article en
4 citations

Abstract

The Internet of Things (IoT) integration in drip irrigation systems is examined in this review paper, with an emphasis on automation, data-driven optimization, and real-time monitoring. The integration of IoT-driven automated drip irrigation represents a transformative leap toward sustainable agriculture, optimizing water conservation, crop productivity, and environmental resilience. Precision farming technologies like artificial intelligence (AI), the Internet of Things (IoT), and 5G-enabled automation are essential for improving irrigation efficiency through real- time monitoring, predictive analytics, and resource optimization as the challenges of climate change and food security worsen. Case studies, including Microsoft’s AI-driven initiative in Andhra Pradesh, have demonstrated significant improvements, such as a 30% increase in yield and up to 70% water savings through IoT-based systems. Machine learning techniques like KNN, SVM, ANN, and RF have further refined irrigation scheduling, achieving over 98% accuracy. This review analyzed 56 peer-reviewed studies published between 2015 and 2024, selected based on relevance to IoT-enabled drip irrigation with particular emphasis on literature from the last three years (2022–2024) with AI/ML integration, and deployment aspects. The key research gap identified is the absence of standardized benchmarks, open datasets, and scalable frameworks, which restrict the transition from pilot-scale studies to large-scale real-world adoption. Despite the advantages, barriers such as high initial investment, connectivity limitations, cybersecure- risks, and technical complexity hinder widespread adoption. Addressing these challenges through cost-effective solutions, policy interventions, subsidies, and farmer training programs will be crucial. The evolution toward Industry 5.0, emphasizing human–machine collaboration, AI-powered AgroBots, and energy-efficient smart irrigation solutions, underscores the future of precision agriculture. Highlights.

Discover AgricultureVol. 3(1)
Institute of Management Technology (IN), Sardar Vallabhbhai Patel University of Agriculture & Technology (IN), Delhi Technological University (IN)
Openalex Percentile: Top 5%
Irrigation Practices and Water Management
4.74
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