Innovations in Embedded Systems for Smart Farming

Recently, the agricultural sector has modernised numerous high-tech processes and equipment to protect against the evolving food crisis. Smart farming is the key to future consumption demands and incorporating various modern techniques to increase production quality and outcome. The recent smart farming initiatives have changed the agricultural field tremendously. Among various innovations coming up, embedded systems and embedded computing networks are some of the recent innovations being implemented for making smart farming approaches more resilient to the environment’s changing factors like climate and natural disasters. To create resilient agriculture for the future, farmers will require unlimited data about environmental factors like temperature, soil health, air quality, humidity, and various other parameters to understand the cultivating space. To help with this, embedded sensors play a significant role in collecting real-time and current farming data from the field to cloud servers. Specialised embedded algorithms are developing today to make accurate predictions of cultivation.

Beyond just gathering the data and distributing it to the servers, embedded systems strategically create digital platforms for farmers to access and view real-time data effectively. The best example for this would be embedded pest control sensors and irrigation sensors to analyse and reduce the use of pesticides and to recognise the water needed for particular crop growth. With these advanced sensors, we will be able to consume safer and healthier food and save a lot of water wasted in agricultural farms, which were done manually earlier. GPS techs have existed for a long time in the industry. However, when integrated with embedded computing systems, they are of more extensive uses, which typically help farmers handle the machinery, visions, automated tractors, robots, and monitoring effectively. Indeed, embedded computing systems have profound benefits in agriculture, which farmers can rely on to ensure high performance. Precision farming has been made possible by developing embedded and big data systems to help drones monitor the crop data and is assisting the farmers in viewing them and being precautious until the harvest. Likewise, the level of nutrition, water, and fertilisers can be easily observed with the help of embedded systems connected to Wi-Fi and the internet. Connectivity might sometimes be a challenge to embedded systems as farmers require seamless internet connectivity. Still, with the approaching modern techs, future embedded systems would be a breakthrough in agriculture. On the whole, this special issue spotlights the responsible innovations of embedded computing systems for developing resilient agriculture for the future. We invite scholars and researchers from this platform to submit more ideas falling under this context.

The subject of knowledge includes, but is not limited to the following:

● Precision farming and digital crop production: Embedded computing systems and their contribution

● Role of embedded technologies for automated trucks and harvesting methods

● Embedded computing algorithms for supporting smart farming: Automation robots and machineries

● Embedded farming technologies for environmental compatibility and sustainable farming

● New frontiers of embedded sensors and systems for food production and smart agriculture

● Intelligent monitoring systems with embedded platforms: Transformation towards agriculture 5.0

● Empowering smart farming solutions with modern embedded systems

● Big data and embedded computing: Smart farming yield estimation and production

● Role of embedded technologies to meet global food supply crisis

● Cloud computing and embedded technologies: Future of smart agriculture practices

● Automated embedded sensor techniques for smart harvesting and improved quality crop outcome

● Smart farming based on embedded computing networks: Growth in developing countries

● Responsible and resilient farming methods: Influence of embedded networks

Guest Editors:

Dr. Olatayo Moses Olaniyan

Department of Computer Engineering,

Federal University, Oye Ekiti, Nigeria

E-mail: olatayo.olaniyan@fuoye.edu.ng, olatayo.olaniyan@outlook.com 

Google Scholar: https://scholar.google.co.in/citations?user=dZnvqUYAAAAJ&hl=en 

Biography: Dr. Olatayo Moses Olaniyan holds a Bachelor of Technology degree in Computer Engineering from Ladoke Akintola University of Technology (LAUTECH), Nigeria. A Master of Technology in Computer Science, and Ph.D. in Computer Science from Ladoke Akintola University of Technology, Nigeria. He is an Associate Professor of Computer Engineering in the department of Computer Engineering, Federal University Oye-Ekiti Nigeria. His research interests include Soft Computing, Embedded Systems, Computer Network, and Human Computer Interaction.

Dr. Temitayo Matthew Fagbola

Centre of Excellence for Data Science,

University of Hull, United Kingdom.

E-mail: temitayo-matthew.fagbola@hull.ac.uk 

Google Scholar: https://scholar.google.com/citations?user=i-paDBQAAAAJ&hl=en&oi=sra 

Biography: Dr. Temitayo Matthew Fagbola (Member, IEEE) received the B.Tech. degree in computer science from the Ladoke Akintola University of Technology, Ogbomoso, Nigeria, in 2007, the M.Sc. degree from the University of Ibadan, Ibadan, Nigeria, in 2011, and the Ph.D. degree in computer science from the Ladoke Akintola University of Technology in 2015. He is currently with the Centre of Excellence for Data Science, Artificial Intelligence, and Modeling (DAIM), University of Hull, United Kingdom. His research interests include smart data mining, AI for social good (AI4SG), natural language processing, and social aspects of human–computer interaction.

Dr. Abayomi Alli

Department of Computer Science,

Federal University of Agriculture, Abeokuta, Nigeria

E-mail: abayomiallia@funaab.edu.ng 

Google Scholar: https://scholar.google.com/citations?user=TASL-CUAAAAJ&hl=en 

Biography: Dr. Abayomi-Alli holds a B.Tech. Degree in Computer Engineering from Ladoke Akintola University of Technology, Ogbomoso, Nigeria, M.Sc. and Ph.D. Degrees in Computer Science from the University of Ibadan, Ibadan, Nigeria and Ladoke Akintola University of Technology, Ogbomoso, Nigeria, respectively. He is presently a Lecturer in the Department of Computer Science at Federal University of Agriculture (FUNAAB), Abeokuta, Nigeria. His research interest includes Pattern recognition, Machine learning, and Microprocessor based system.

Important Dates:

Submission deadline: March 10, 2025

Author notification: May 25, 2025

Revised papers due: July 30, 2025

Final notification: October 05, 2025