AI based smart precision agriculture using Embedded IoT for sustainable environment

Deep learning has emerged with big data technologies and IoT to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. The deep learning can be applied in various precision farming towards crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition The filtering and classification makes the precision agriculture to benefit with deep learning technologies. By applying deep learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.​

Smart Farming is a modern system of doing agriculture and improving cultivation in a sustainable way. The smart IoT devices are connected together with innovative technologies to enhance the agriculture. The smart sensors deployment and the smart way of agriculture enhances the agriculture with less physical work from farmers and thereby productivity is increased. The smart technologies enrich the agriculture with less utilisation of water, less electricity usage and more optimisation with real time monitoring in humidity and temperature. In IoT based smart farming, the field monitoring is done with multiple sensors like humidity, temperature and soil moisture. There remains a lack of research and development in relation to Smart Sustainable Agriculture (SSA), accompanied by complex obstacles arising from the fragmentation of agricultural processes like the control and operation of IoT/AI machines, data sharing and management, interoperability and large amounts of data analysis and storage.

This special issues focuses high quality, Novel solutions and research for smart and deep learning based sustainable agriculture, ranging from IoT, WSN, efficient sensing, cloud/edge computing, smart actuators, etc

List of Topics but not limited:

Topics of interest include, but are not limited to, the following scope:

• Smart farming, precision agriculture, and phenotyping using Embedded IoT
• Smart applications for site-specific crop monitoring and management using deep learning based Embedded IoT
• Data processing techniques and related big data problem and solution using deep learning based Embedded IoT
• Embedded IoT solutions and automation using deep learning based Embedded IoT
• Data-aware networking in smart agriculture using deep learning based Embedded IoT
• Sensor network deployment for smart agriculture using deep learning based Embedded IoT
• Smart sensors, sensing mechanisms and platforms for sustainable agriculture using deep learning based Embedded IoT
• Internet of Things (IoT) for smart agriculture using deep learning based Embedded IoT
• Cloud-enabled techniques and innovation for sustainable agriculture using deep learning based Embedded IoT
• Big data innovation in sustainable agriculture using deep learning based Embedded IoT
• Decision support systems and making (AI, machine learning) for sustainable agriculture using deep learning based Embedded IoT
• Real time monitoring in smart agriculture using deep learning based Embedded IoT
• Growing trends of precision agriculture using deep learning based Embedded IoT
• Emerging tools and techniques for precision agriculture using deep learning based Embedded IoT

Provisional deadlines:

Submission deadline: May 15, 2021

Acceptance deadline: March 15, 2022

Guest Editors:

1. Dr.S.Vimal (Lead Guest Editor)

Department of Information Technology,

National Engineering College, K.R.Nagar, Kovilpatti,

Tamil Nadu, India

Email: svimalnec@nec.edu.in,svimalphd@gmail.com

2. Dr.Danilo Pelusi

Faculty of Communication Sciences,

University of Teramo, Via Balzarini, 1, 64100

Teramo, Italy

Email: dpelusi@unite.it

3. Dr. Shahid Mumtaz

Senior Researcher, Senior Member IEEE

ACM Distinguished Speaker

Editor in Chief – IET Journal of Quantum Communication

Instituto de Telecomunicaces, Aveiro, Portugal

Email: smumtaz@av.it.pt

4. Dr. A. Suresh

Nehru Institute of Engineering and Technology,

Coimbatore,

Tamilnadu,

India

Email: prisu6esh@ieee.org

5. Dr.Smail Niar,

Professeur, LAMIH UMR CNRS

Université Polytechnique Hauts-de-France

Campus Mont Houy – LAMIH JONAS

59313 Valenciennes Cedex 9, France

Email: smail.niar@uphf.fr

6. Dr.Mazen A. R. Saghir

Associate Professor of Electrical and Computer Engineering

Department of Electrical and Computer Engineering

American University of Beirut

Beirut

Email: mazen@aub.edu.lb