AI and Big Data-Driven Development of Smart Agriculture
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".
Deadline for manuscript submissions: 20 February 2027 | Viewed by 172
Editors
Interests: smart agriculture; machine learning; crop modeling; remote sensing; digital agriculture
2. International Center for Agricultural Research in the Dry Areas (ICARDA), Cairo 11742, Egypt
Interests: smart agriculture; precision agriculture; artificial intelligence; machine learning; crop modeling; remote sensing; climate-smart agriculture; digital agriculture; geospatial analytics; decision support systems
Special Issue Information
Dear Colleagues,
The rapid advancement of Artificial Intelligence (AI), Big Data analytics, remote sensing, the Internet of Things (IoT), cloud computing, digital twins, and decision-support systems is transforming agriculture into a more efficient, resilient, and sustainable sector. Modern agricultural systems generate vast amounts of heterogeneous data from satellites, drones, field sensors, machinery, weather stations, crop models, and farm management platforms. Harnessing these data streams through advanced analytics and AI-driven approaches provides unprecedented opportunities to improve productivity, resource-use efficiency, climate resilience, and food security.
This Special Issue will bring together cutting-edge research and practical applications demonstrating how AI and Big Data technologies can accelerate the development of smart agriculture. We invite contributions addressing machine learning, deep learning, generative AI, large language models (LLMs), multi-agent systems, computer vision, digital agriculture platforms, geospatial analytics, crop modeling, digital twins, precision agriculture, climate-smart agriculture, agricultural robotics, and intelligent decision-support systems. Particular attention will be paid to studies integrating multiple data sources, including remote sensing, IoT networks, climate data, soil information, and farm management records, to support real-time monitoring, forecasting, optimization, and risk assessment. Applications may include yield prediction, disease and pest detection, irrigation scheduling, nutrient management, greenhouse automation, climate adaptation, carbon accounting, and agricultural sustainability assessment.
The Special Issue encourages the submission of interdisciplinary contributions from academia, industry, governmental organizations, and international research institutions. By bridging advances in AI, data science, and agricultural sciences, this Special Issue will highlight innovative solutions supporting a transition toward data-driven, sustainable, and climate-resilient agricultural systems worldwide.
Dr. Ahmed Attia
Prof. Dr. Ahmed Kheir
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- big data analytics
- smart agriculture
- precision agriculture
- machine learning
- deep learning
- digital twins
- remote sensing
- internet of things (IoT)
- decision support systems
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