Aims and Scope

The Artificial Intelligence in Agriculture Systems (AIAS) is an international, peer-reviewed journal dedicated to advancing the integration of artificial intelligence (AI), Internet of Things (IoT), and data analytics in the agricultural domain. The journal provides a scholarly platform for the dissemination of original research, technological innovations, and applied methodologies that foster the digital transformation of agriculture toward sustainability, efficiency, and resilience.

In an age where global food security, environmental sustainability, and resource optimization are pressing challenges, AIAS promotes interdisciplinary collaboration across the fields of computer science, agronomy, data science, robotics, and environmental engineering. The journal encourages contributions that utilize intelligent systems to address complex agricultural problems — from crop management and soil health to climate adaptation and supply chain optimization. AIAS serves as a global forum for academics, practitioners, and policymakers to exchange ideas, develop frameworks, and share evidence-based insights that drive the future of smart, data-driven agriculture.

The Artificial Intelligence in Agriculture Systems (AIAS) is dedicated to advancing the use of artificial intelligence, IoT, and data analytics in transforming agriculture toward sustainability and efficiency. The journal serves as a platform for sharing innovative research, applied technologies, and best practices that enable data-driven and environmentally friendly agricultural systems.

Topics of interest include (but are not limited to):

• Adaptive learning systems, intelligent tutoring, and personalized education

• AI and machine learning applications in precision agriculture and smart farming.

• Computer vision and sensing technologies for crop health monitoring and yield prediction.

• Agricultural robotics, automation, and unmanned aerial systems (UAS) in farm management.

• Climate-smart and sustainable agriculture supported by intelligent technologies.

• Data-driven decision support and predictive analytics for agricultural policy and management.