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. 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.