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Ensuring the safety, security and sustainability of our food, agricultural and water resources represents one of this century’s great challenges. While ever evolving, our understanding of these complex systems and their interactions remains imperfect, as is our capacity to model, observe, and then interpret their behavior and response.
The rapid growth in our monitoring capacity, driven by advances in earth observation, crowd-sourced data, the Internet of Things and other data-harvesting frameworks, have enabled a paradigm shift in how information can be obtained, interrogated and disseminated, and provided an entirely new basis for decision making and management. However, our ability to differentiate the "signal from the noise" is becoming increasingly difficult, as the rates of both data collection and storage increase exponentially, making the interpretation and analysis of these vast information streams difficult.
In this era of big data, Artificial Intelligence offers a means to mine these rich data reserves for information resources, turning an overwhelming deluge into a stream of useable content, and enabling more effective monitoring and management of our natural and engineered systems. More importantly, AI seeks not only to mimic, but to advance upon the human capacity to perceive, recognize and predict outcomes from processes that are often clouded in uncertainty. The promise of AI is to deliver upon these needs, offering pathways for deeper insights, predictive ability and autonomy.
AI in Food, Agriculture and Water provides a forum to demonstrate the potential of advanced analytical tools to navigate this rapidly changing landscape of digital informatics. The journal’s Specialty Section will publish research at the cutting-edge of AI, with a focus on showcasing the delivery of AI driven insights within these interlinked disciplines. Topics exploring novel applications using machine learning, computer vision, robotics and automation and other AI approaches are all welcome, as are those that demonstrate system innovations, optimization, or improved understanding across the food, agriculture and water sectors.
Indexed in: Google Scholar, DOAJ, CrossRef, Digital Biography & Library Project (dblp), CLOCKSS, OpenAIRE
AI in Food, Agriculture and Water welcomes submissions of the following article types: Brief Research Report, Conceptual Analysis, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Policy and Practice Reviews, Review, Specialty Grand Challenge, Systematic Review and Technology and Code.
All manuscripts must be submitted directly to the section AI in Food, Agriculture and Water, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
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