AIM (Agroecological Information Model)
by Paul Chaney, Adam Russell and Dr Andrew Omerod
AIM is a prototype intelligent software tool for automating the design and ongoing management of human-scale agroe-cological food systems.
Agroecological systems (permaculture, forest gardening and other types of perennial horticulture) utilise plant and insect polycultures to generate biodiversity and soil fertility alongside the production of human food. Agroecology suits the creation of ‘edible urban landscapes’ and ‘food forests’. In conjunction with other forms of urban growing, edible urban landscaping can reduce food miles and a city’s overall carbon footprint as well as improve urban food sovereignty.
AIM will use bespoke algorithms and machine learning to automatically design complex plant polycultures to match local conditions and local needs, allowing increased urban food production in existing green and brownfield sites with minimal inputs of water, fertiliser, herbicides and pesticides, and human labour.
AIM will use a site-specific predictive growth model to provide automated horticultural decision-support allowing local communities to manage the edible urban landscape themselves. AIM will encourage communities to participate in food growing no matter how inexperienced they are, unlocking associated well-being benefits, generating community activities, and providing opportunities for education.
AIM will allow city authorities to implement urban food production on a large scale, and allow urban planners and developers to include community managed edible urban landscaping in their design offer for the benefit of human and non-human communities alike.
About the Team
Paul Chaney is a contemporary artist exploring post-collapse food systems using digital technology and public participation.
Adam Russell is a software developer and AI specialist focusing on creative improvisation between machine and living systems.
Dr Andrew Omerod is an economic botanist interested in novel food crop supply chains, community development and diversity in farming systems.