Date: 15 February 2012
Time: 13.00-14.00
Place: NJ14 3-228 (Las Vegas)
In agricultural production, weeds compete with crops for nutrition, sunlight and water. If uncontrolled, they may outnumber the crop plants and cause heavy loss to the yield. In order to control weeds, typical method is application of herbicide chemicals. Indiscriminate use of chemicals, on the other hand, is detrimental to environment and a lot of research has been done to control the quantities of chemicals in farming. Still, the utmost precision of applying the herbicides only when and where needed (known as Site Specific Weed management ‐ SSWM) is a big challenge because it requires an automatic computerized system of constantly monitoring all the plants in a field.
The research under this PhD study addresses this problem. It is focused at developing tools for an autonomous ground vehicle (robot) for weed detection in sugar beet fields using 3D image data. So far, predominantly 2D images have been used in research in weed detection. A prerequisite for use of 2D data is that the individual plants are well separated not occluding each other. When plants grow in size, overlapping canopies make this approach difficult to resolve plants and analyze their structures. In such a case, 3D data will be beneficial. In this way, this study will help in localized spray of chemicals, hence reducing the amount of chemicals used in farming using image processing and computer vision.
This PhD study is financed by Danish Council of Strategic Research under project ASETA (Adaptive Surveying and Early treatment of crops with a Team of Autonomous vehicles) which also involves aerial vehicles and aerial imaging. Major research work will be carried out at Department of Architecture and Media Technology, Aalborg University, Aalborg, while the test campaigns will take place in the fields prepared by University of Copenhagen, Department of Agriculture and Ecology at Taastrup.