Data-Driven Agricultural Innovations with Artificial Intelligence and Industrial Internet of Things (IIoT)

Dear Colleagues,

The agricultural sector has a rich history of adopting novel technologies to boost productivity, decrease risk, and improve sustainability. There is a growing trend in the use of digital and communications technologies by farmers and policy makers alike to address issues that have arisen due to climate change, scarcity of resources, a rising global population, disruption of supply chains by the pandemic and natural disasters, etc., which hamper efficiency and significantly impact business models in the agricultural sector. The opportunities presented by such technological integration are numerous, but they also come with new and evolving challenges.

In both industry and academia circles, new research and development initiatives have been proposed and undertaken to address many of these issues. Innovations including real-time monitoring and controlling crop irrigation systems via smartphones, crop sensors, intelligent livestock farming technology using UAV-based computer vision and deep AI technologies, farm automation, indoor vertical farming, modern greenhouses, precision agriculture, blockchain for distributed data sharing and commodity exchange, and access to the industrial internet of things through the fog and edge computing, have all benefited the agricultural sector. One shared characteristic of these technologies is the important role that "data" plays in driving their success by achieving the proposed benefits. 

This Special Issue will showcase "data-driven agricultural innovations" across different data scales and resolutions using artificial intelligence and the industrial internet of things (IIoT). We welcome contributions that address topics including, but not limited to key and emergent R&D issues for agricultural innovations across the edge-, fog-, and cloud-layered architectures and computing, hyperspectral and multispectral remote sensing, autonomous robotics and computer vision systems, multisensor data filtering, and fusion, big data processing and analytics, digital twin technology, intelligent logistics and supply chain management, complex systems modeling and simulation, risk assessment, prediction, and decision analysis through applying scientific approaches, and methodologies from multiple disciplines, including artificial intelligence, computer vision, machine learning, robotics, cyber-physical systems, cloud and edge computing, cyber security, blockchain, data science, computational science, operations research, remote sensing, agro informatics, crop science, and animal science.

Prof. Dr. Paul Kwan
Prof. Dr. Wensheng Wang
Guest Editors

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