Innovations in Smart Agriculture: Advanced Sensors and AI Systems Powered by Quantum and Classical Machine Learning
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".
Deadline for manuscript submissions: 25 July 2026 | Viewed by 25
Special Issue Editor
Special Issue Information
Dear Colleagues,
Rapid advancements in sensor technologies and the rise of artificial intelligence (AI) are ushering in a new era of intelligent systems for smart agriculture and precision farming. This Special Issue invites submissions of original research, reviews, and case studies on the integration of classical and quantum machine learning (ML) algorithms into AI-enabled agricultural systems. While conventional ML techniques are maturing to optimise crop yield prediction, soil health monitoring, and efficient resource management, quantum machine learning (QML) offers a promising frontier for processing complex agricultural data at scale with unprecedented speed and accuracy. With next-generation sensors and IoT devices continuously collecting multi-modal environmental data, AI-driven decision-making systems will play a crucial role in supporting sustainable farming practices, minimising environmental impact, and building resilience in food production. We invite submissions that cover a range of topics, including novel ML/QML algorithms, sensor networks for data collection and monitoring, field-deployable systems, AI interpretability, theoretical foundations, and applications in diverse agricultural settings. Papers that showcase interdisciplinary innovation and real-world applicability are highly encouraged.
Dr. Ahsan Morshed
Guest Editor
Manuscript Submission Information
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Keywords
- smart agriculture
- precision farming
- quantum machine learning
- classical machine learning
- intelligent sensor systems
- agricultural IoT (Internet of Things)
- crop monitoring and prediction
- AI in agriculture
- data-driven farming
- sustainable agricultural technologies
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