Growing demands for food and energy due to population growth, rapid urbanization, changing diet and economic development have placed tremendous pressure on agriculture and natural resources. This has resulted in expansion of agricultural lands, and intensification of agricultural practices, which are major sources of increased soil erosion, water pollution, loss of biodiversity and habitat, and greenhouse gas emissions. In Ag Sensing lab, we use remote sensing, geographic information system (GIS), data analytics/machine learning, precision agriculture technologies, and ecosystem models to develop decision support systems that farmers could use to promote sustainable agricultural production. Our vision is of an agricultural landscape in which farmers produce more food and feedstock while respecting nature and mitigating environmental problems.
In addition to using freely available data on weather and landscape, collected at different geographic scales, we use advanced and emerging technologies, such as GIS, global position system (GPS) and remote sensing (e.g., sensors on broad drones, aircraft, satellite, and ground) to collect data. Through use of machine vision, advanced image processing, machine learning and ecosystem models, we try to understand and quantify ecosystem services (e.g., crop and soil health, biomass, water quality, greenhouse gas emission (GHGe)) under various land management practices at geographic scales, ranging from field to watershed to regional to national scales.
We work closely with researchers from various disciplines (e.g., computer science, agricultural, mechanical and electrical engineering, economics, crop and horticulture, and sustainable engineering), farmers, and industries to identify tipping point questions and develop decision support tools. Our long-term goal is to develop decision support tools for use by various stakeholders, including farmers, environmentalists, and policymakers to promote use of sustainable agricultural practices. We are also committed to communicating our science through journal articles, factsheets, reports and media for a wide variety of audiences.