IEEE Symposium on Computational Intelligence in Agriculture (CIAg)

Accurate information about the physical, chemical, and biological components of agricultural and natural ecosystems is critical for our understanding and achieving of breakthroughs necessary to maintain healthy terrestrial and aquatic ecosystems.  For instance, meeting the food, feed, fiber, and energy needs of an ever-increasing global population represents a formidable challenge for agriculture and puts strains on natural ecosystems. Improvements in crop and animal genetics and management are essential to sustain agricultural production while protecting natural ecosystems and water resources.  The complexity of the challenges we are facing require interdisciplinary approaches to develop mechanistic understanding of processes at different levels of organization, and their interconnections in time and across scales.

This symposium (SSCI-CIAg) seeks to bring together innovation in intelligent sensors and novel platforms, eXplainable artificial intelligence (XAI), and artificial intelligence methods and implementation for agriculture. These goals address the spectrum of AI methods, from intelligent sensing to visualization, decisions making, and actions, with an emphasis on trustworthy and transparent human-in- and human-over-the-loop solutions to agricultural practices.  To increase cross- and inter-disciplinary activities in the world and help position researchers from disparate disciplines for collaborative research to tackle grand challenges in agricultural and natural ecosystem sustainability.

Topics

We solicit papers in on all aspects of artificial intelligence methods in managing, monitoring, exploring and improving food production while protecting natural resources. In particular, we welcome studies at the intersection of multiple disciplines, including robotics, sensing, phenomics, genomics, and Ag in general, with artificial intelligence. The topics of interest include, but are not limited to:

• AI for seasonal forecasting models, improve yielding prediction of healthier and more productive crops
• AI in pest control and prediction of environmental impacts
• AI for monitoring soil and growing conditions
• AI for farmers, harvesting and precision Ag
• AI to help with workload, and improve agriculture-related tasks in the entire food supply chain
• AI and autonomous systems for field phenotyping (UAVs, ground, remote, etc.)
• AI and Machine learning methods for data analytics in Ag/NR
• AI in animal sciences

Symposium Chairs

Programme Committee

Malia Gehan
Jian Jin
Ian Stavness
Hanno Scharr
Sotirios Tsaftaris
Tony Pridmore
Toni Kazic
Tushar Das Nakini
David G. Mendoza
Andrew Buck
Charlie Veal