AgTech that doesn’t cost the Earth: Creating sustainable, ethical and effective agricultural technology that enhances its social and ecological contexts

Abstract

To feed the growing human population we require increased food production and security, while using less land and causing less environmental damage. Significant changes in agriculture are needed to meet these demands. One widely touted solution is smart, AI-enhanced Agricultural Technology. In this article we argue that improved technology is insufficient to address the needs of many farmers, but that by taking a whole-of-system approach native to Artificial Life we can shift towards creating sustainable, ethical and effective AgTech. This can innovate industrial agriculture in developed nations and benefit small landholders from vulnerable communities, whilst reducing the environmental impacts of food production globally.

Using AI to Improve Sustainable Agricultural Practices: A Literature Review and Research Agenda

“…Expert systems are reported to be an efficient tool for complementing agricultural workers' decision-making , Hungilo, Emmanuel & Emanuel, 2019. Autonomous vehicles, agent-based systems, and robots are also evident in complementing existing farmlevel models and driving sustainable agricultural practices , Dorin, Penn & Siqueiros, 2022Fikar, 2018). Additionally, the digital twin as a virtual, real-time equivalent of AgriDSS is being actively explored in the agricultural domain to boost the effectiveness and efficiency of agricultural supply logistics Galaz et al, 2021;Linaza et al, 2021).…”

“…Abbasi , Fikar, 2018, Giusti & Marsili-Libelli, 2015, Orn et al, 2018, Viani et al, 2017 Supply chain efficiency Araújo et al, 2021 Waste reduction Bonaccorsi et al, 2017, Fikar, 2018, Panchbhaiyye & Ogunfunmi, 2018 Environmental sustainability Human-in-theloop Dorin et al, 2022, Viani et al, 2017, Vinuesa et al, 2020 Human-centered AI , Fikar, 2018, Klerkx & Rose, 2020, Ooge & Verbert, 2022, Orn et al, 2018, Panchbhaiyye & Ogunfunmi, 2018, Roshanianfard et al, 2020, Singh, 2018, Vinuesa et al, 2020 Table A6: Results for social roadblocks Trust , Ooge & Verbert, 2022, Orn et al, 2018 AI unawareness Atwal et al, 2021, Marinoudi et al, 2019, Ooge & Verbert, 2022, Orn et al, 2018, Santos Valle & Kienzle, 2020 AI use uncertainty , Klerkx & Rose, 2020, Ooge & Verbert, 2022, Kumar et al, 2020…”