Challenges and opportunities of AI adoption for agri-food system sustainability: Environmental footprint reduction

Socio-economic disparities, coupled with resource (e.g., water, fertilizers, arable land) constraints, and climate change have complicated the ability of current agri-food systems to feed a projected global population of 9.2 billion by 2040. These pressures have led to the intensification of agricultural management practices, and consequently, environmental degradation through soil loss, water pollution, loss of biodiversity, and greenhouse gas emissions. As such, there is a need to identify solutions that can reduce the environmental footprints while improving the productivity of agri-food systems. Artificial intelligence (AI)-driven technology has emerged as an option for optimizing agri-food systems. AI technology provides a suite of tools, e.g., machine learning, predictive modeling, Internet of Things (IoT) integration, robotics, that indicate a best-case scenario improvement in crop productivity by about 30% (for one study) along with up to 27% and 57% reduction in water and energy usage, respectively, for another study. However, some technical, socio-economic, ethical, and regulatory challenges still need to be overcome before the full potential of AI-driven technology in crop production can be realized. Concurrently, there are opportunities to improve the adoption of AI-driven technology through public-private partnerships, producer education, and a supportive policy framework. This review explores the challenges and potential opportunities of AI in promoting sustainable agriculture, while critically providing strategies for reducing the environmental footprint of this technology.