Artificial Intelligence (AI) Research Institutes
“This major Federal investment in next generation agriculture signals our commitment to keeping American agricultural innovation on the leading edge of global science,” said USDA-NIFA Deputy Director Parag Chitnis. “These future-focused centers of innovation will use the latest techniques from all corners of science to seek solutions for myriad challenges facing agriculture, from climate resilience to crop improvement and animal welfare to labor shortages and farm safety.” (USDA-NIFA Associate Director Parag Chitnis).
By 2050, agriculture will need to produce 70% more food over current levels, while also reducing degrative impacts on the environment. Not only is the demand for food growing, so is agricultural system complexity. It is imperative that we have resilient and robust food systems that work across national and continental boundaries and meet climate-change goals. Advancing and deploying new approaches and applications of AI is a natural extension to agricultural production and food security. There are numerous opportunities to apply transformative, user-inclusive data-driven research methods and algorithm development to the food and agricultural sector to yield meaningful insights, predictive tools, and real-time solutions for production; food processing; transportation and storage; wholesale and retail marketing; and high-quality products and information for consumers.
Strategic use of AI throughout agriculture and food production systems may spur the next revolution in food and feed production. Food production has been greatly enhanced over the past several decades, resulting in greater food security, human health, employment, and overall quality of life; however, there have been unintended consequences impacting natural resource use, water and soil quality, and pest population expansion. An AI-based approach to agriculture can go much further to address whole food systems, inputs and outputs, internal and external consequences, and issues and challenges at micro, meso, and macro scales that include meeting policy requirements for ecosystem health.
There are critical challenges associated with the adoption of AI in agriculture. The success of AI will depend on engaging and connecting stakeholders. Social engagement on the processes and products of AI will be critical for assessing social acceptance and implications of the rapid expansion of technology. While challenges in methods, data, privacy, and fairness are universal to the broader AI endeavor, these considerations take on particular urgency when associated with a need as fundamental as the food supply. AI Research Institutes that simultaneously advance foundational AI research and agriculture and food systems might address a wide range of research foci, build new multidisciplinary communities, and create the workforce needed for an AI-powered revolution in agriculture.