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  • Publication | 2025
Integrating IoT With Machine Learning and Deep Learning Models for Precision Soil-Less Agriculture: A Review
Global Food and Nutrition Security

The rising global population, along with the challenges faced by conventional agricultural practices, has intensified the demand for smart agricultural solutions. In modern agriculture, the integration of Internet of things and machine learning-deep learning has evolved as an irreplaceable force, particularly in the context of soil-less farming systems. This study aims to explore the potential of IoT and intelligent data-driven technologies in advancing smart and precise soil-less agriculture systems and the review commences by scrutinising the basic principles of IoT and its application in soil-less agriculture. It also emphasises the integration of diverse sensors for real-time data collection on dynamic environmental and plant parameters. The advantages of IoT and machine learning–deep learning in soil-less agriculture systems are comprehensively analysed, covering numerous applications. This study also recognises challenges, such as data security and privacy concerns, and interoperability concerns which must be addressed for wider adoption and sustainable growth. Overall, this review paper presents a comprehensive assessment of IoT and ML-DL in soil-less agriculture systems. It highlights the potential of these technologies in tackling the key challenges faced by modern soil-less agricultural systems. Ultimately, these advancements can contribute significantly towards a greener and sustainable future for agriculture.