I-Corps: 3-Dimensional Data Output from Remote Sensing Algorithms

University Of Toledo, Toledo OH

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is to develop a 3-dimensional view of agricultural fields using algorithms applied to remotely sensed imagery to help farmers enhance soil fertility and crop production while also protecting watersheds. From an economic perspective, farmers want to minimize expenditures on nutrients. There is a need by farmers to increase below ground drainage through installation of tiles, perforated plastic pipes, and thus increase production. Tile placement and integrity, coupled with existing data on soil type and characteristics, have a critical impact on the hydrology and fertility of fields, affecting plant growth, the need for fertilizer/nutrients, pesticide and other types of applications. Precise knowledge of a three dimensional view of the land will enable farmers to make appropriate decisions about the application of fertilizers, micro-nutrients, pesticides, etc. to minimize cost, maximize revenue and do as little harm as possible to the ecosystem in which the farm exists. Bringing together data layers of agricultural fields along with information on tile drains will allow the agricultural industry to optimize production through proper placement and repair of tiles while controlling nutrient use and loss. This I-Corps project uses remote sensing techniques with aerial imagery to provide information precisely identifying the location of drain tiles buried beneath farm fields. Farmers have expressed that knowledge of the location of tile reduces their costs when tile fail and need repair and also helps when additional tile are added to a field improving drainage. In previous work, it was found that fertilizers applied to agricultural fields will pass through the soil and exit a field through tile drains to the surface water system. This I-Corps project uses remote sensing algorithms to detect tile drains below the soil. The technique utilizes the reflectance of the soil as well as the linear shape expressed on the soil surface that indicates tiles within fields. The information generated through this project is unique and much needed by the agricultural industry to better utilize land in crop production "precision farming".