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.<UnorderedList Mark="Bullet"> <ItemContent> High-Accuracy Machine Learning Models for Irrigation Optimization Recent studies demonstrate that AI and ML models, particularly Random Forest and Artificial Neural Networks, can optimize irrigation with up to 99.8% accuracy, resulting in water savings of 37% to 70%. However, real-world scalability and adaptability remain ongoing challenges. </ItemContent> <ItemContent> AIoT Integration for Real-Time, Precision Agriculture The integration of AI, IoT, and 5G technologies—such as AgroBots, ROS-based irrigation robots, and ZigBee mobile irrigation systems—enables real-time crop monitoring, soil assessment, and automated irrigation, showcasing the potential of AIoT-driven smart farming to enhance productivity and sustainability. </ItemContent> <ItemContent> Key Research Gaps: Standardization, Adaptability, and Field Validation Despite technological advancements, gaps persist in platform standardization, real-time adaptive irrigation models, and long-term cost–benefit analysis. These limitations hinder large-scale deployment, especially in rural and resource-constrained regions. </ItemContent> </UnorderedList>

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

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2025年11月19日

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Nikita Jaiswal,
T. Vijay Kumar,
Chitra Shukla

摘要

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.

High-Accuracy Machine Learning Models for Irrigation Optimization Recent studies demonstrate that AI and ML models, particularly Random Forest and Artificial Neural Networks, can optimize irrigation with up to 99.8% accuracy, resulting in water savings of 37% to 70%. However, real-world scalability and adaptability remain ongoing challenges.

AIoT Integration for Real-Time, Precision Agriculture The integration of AI, IoT, and 5G technologies—such as AgroBots, ROS-based irrigation robots, and ZigBee mobile irrigation systems—enables real-time crop monitoring, soil assessment, and automated irrigation, showcasing the potential of AIoT-driven smart farming to enhance productivity and sustainability.

Key Research Gaps: Standardization, Adaptability, and Field Validation Despite technological advancements, gaps persist in platform standardization, real-time adaptive irrigation models, and long-term cost–benefit analysis. These limitations hinder large-scale deployment, especially in rural and resource-constrained regions.