Integration of Wireless Sensor Networks, Internet of Things, Artificial Intelligence, and Deep Learning in Smart Agriculture: A Comprehensive Survey

Integration of Wireless Sensor Networks, Internet of Things


  • Mushtaque Ahmed Rahu Mushtaque QUEST Nawab Shah




This survey explores the synergistic integration of Wireless Sensor Networks (WSNs), the Internet of Things (IoT), Artificial Intelligence (AI), and Deep Learning (DL) in the realm of smart agriculture (SA). The agricultural sector is undergoing a transformative paradigm shift, leveraging advanced technologies to enhance efficiency, productivity, and sustainability. WSNs serve as the backbone, facilitating real-time data acquisition from various sensors deployed in the field. IoT seamlessly connects these sensor nodes, creating a dynamic and interconnected agricultural ecosystem.

The survey delves into the application of AI and DL techniques to process the vast datasets generated by WSNs and IoT devices. Machine Learning (ML) algorithms enable predictive analytics for crop management, disease detection, and optimal resource utilization. DL models, with their ability to extract intricate patterns from data, play a pivotal role in image recognition for crop monitoring and yield prediction.

Furthermore, the survey outlines the key challenges and opportunities in deploying these technologies in SA, including energy efficiency, scalability, and data security. It discusses current trends, emerging technologies, and potential future developments in this interdisciplinary field.

In conclusion, this comprehensive survey provides a holistic overview of the integration of WSNs, IoT, AI, and DL in SA, highlighting the transformative impact on farming practices. The synthesis of these technologies holds the promise of ushering in a new era of precision agriculture, fostering sustainable practices, and ensuring food security for a growing global population.