Los puntos clave no están disponibles para este artículo en este momento.
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.
Jaiswal et al. (Wed,) studied this question.