A smart-sensor approach to automating and optimizing agricultural water reuse

Access to clean, reliable water supplies is critical to our quality of life and our economy, yet across the country millions of Americans do not have access to groundwater that meets drinking water standards. The contaminants that plague drinking water range from common water toxins, such as arsenic and cadmium, to excess nutrients (nitrogen and phosphorus), which are particularly problematic in agriculture regions. Over the next thirty years, the world's population is expected to reach at least 10 billion people, which will create an increase in demand for food and other products that cannot be met with traditional agricultural approaches. In fact, agricultural resources are almost already completely exploited, with very little arable land left to farm, and climate change and population sprawl pose impending threats that will need to be met with rapid innovation. Ultimately, drastic changes in agrotechnology are needed to sustainably source critical food supplies and other essential products. Recent advances in sensing and machine learning technologies are opening up the possibility for "smart sensor" approaches to automation and optimization of agricultural resources that could have a significant impact on our ability to meet the demands of a growing population while addressing the need for sustainable farming. While the emergence of these technologies is exciting, innovative projects are needed to integrate state-of-the-art approaches into simple, user-friendly systems that lend themselves to rapid and widespread adoption. Our proposal aims to meet this challenge by combining a state-of-the-art real-time nutrient sensor with an artificial intelligence platform that will incorporate multiple streams of data to provide a "drop in" technology that can integrate with almost any type of low-input water reuse technology to enable simple, affordable, and sustainable water reclamation for a broad range of agricultural applications.