Guest Editor(s)
Dr. Amna Ikram
Email: amnaikram@gscwu.edu.pk
Affiliation: Department of Computer Science and Information Technology, Government Sadiq College Women University, Bahawalpur, Pakistan
Homepage:
Research Interests: machine learning, deep learning, image processing, smart agriculture, event detection, smart health applications
Dr. Mudassir Khan
Email: mkmiyob@kku.edu.sa
Affiliation: Department of Computer Science, Applied College Tanumah, King Khalid University, Abha, Saudi Arabia
Homepage:
Research Interests: artificial intelligence, machine learning, deep learning, healthcare, medical imaging
Dr. Varun Malik
Email: varun.malik@chitkara.edu.in
Affiliation: Institute of Engineering and Technology, Chitkara University, Punjab, India
Homepage:
Research Interests: deep learning, neural network, deep learning models, financial data
Summary
The world faces an unprecedented convergence of challenges — food insecurity, climate change, and rapid population growth — that demand smarter, data-driven solutions in agriculture. Simultaneously, the rise of AI-driven smart farming, IoT-based field monitoring, and cloud-enabled precision agriculture has created a timely opportunity to harness intelligent computing for real-world agricultural transformation. These pressures make the intersection of AI and agriculture not only relevant but urgent.
This Special Issue aims to consolidate state-of-the-art research advancing both the theoretical foundations and practical deployment of intelligent computing systems in agriculture. We welcome contributions on AI for crop monitoring and disease detection, IoT/edge-based smart farming, precision agriculture systems, agentic AI for autonomous farm management, and cloud computing infrastructures supporting large-scale agricultural data processing. We particularly encourage submissions addressing real-world constraints such as limited labelled data, field deployment challenges, and computational efficiency.
Suggested themes include, but are not limited to:
• Deep learning for crop disease detection, yield estimation, and soil analysis
• Computer vision for crop monitoring, weed detection, and field imaging
• IoT and edge intelligence for real-time field monitoring and smart irrigation
• Agentic AI for autonomous farm management, livestock monitoring, and adaptive irrigation
• Precision agriculture: soil health assessment, crop yield prediction, and resource optimization
• Cloud computing and federated learning for scalable and privacy-preserving agricultural AI
Keywords
intelligent computing, deep learning, computer vision, federated learning, edge intelligence, smart agriculture, crop monitoring, precision farming, explainable AI, transfer learning, trustworthy AI, neural networks
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