Discrimination between and geolocation of cotton and palmer amaranth using spectral and geometric data


The discrimination between cotton and the invasive Palmer amaranth is economically important, as these weeds take resources away from cotton, resulting in diminished crop yield. There has been research into the discrimination between species of plants, including cotton and Palmer amaranth, that focused on the use of aerial imagery and the derived Red, Green, and near-infrared (RGN) spectral data fed into a machine-learning algorithm to classify these plants based on the measurable differences in their spectral characteristics. We believe that this research can be expanded upon by using geometric data derived from the aerial imagery to classify cotton and non-cotton plants based on their physical characteristics. This would also allow for accurate geolocation of the classified weeds for later removal. An autonomous drone with a GPS and a RGN camera attached will take a predetermined path to scan a crop field, and the resulting videos will be divided into individual frames. From these frames, both the RGN spectral data and a 3D point cloud can be derived. The RGBN data and the geometric data will be fed into a machine learning algorithm for classification between the cotton and non-cotton plants, and then additional processing will be done to geolocate the weeds. With this additional information for classification, it is hoped that the discrimination between cotton and weeds can be more accurate, and the location of the weeds can be more exact.

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering