Plant Density Estimation and Weeds Mapping on Row Crops at Emergence Using Low Altitude UAS Imagery
McCraine, C., Samiappan, S., Prince Czarnecki, J. M., & Dodds, D. M. (2019). Plant Density Estimation and Weeds Mapping on Row Crops at Emergence Using Low Altitude UAS Imagery. Proceedings Volume 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV. Baltimore, MD: Society of Photo-Optical Instrumentation Engineers. 11008, 110080Y. DOI:10.1117/12.2520252.
Plant density estimation during the emergence phase is critical for early-season decision making. Estimation of both crop and weed density is critical for addressing early season issues. Mapping of weeds in crops at any stage can be useful; however, early competition from weeds is frequently most detrimental to yield. The objectives of this study were to develop a set of algorithms that accurately estimated the crop and weed density at emergence from sUAS imagery, and to do so using methods which were operationally feasible on production-field scale. The areas of interest were Mississippi cotton fields, where weeds were present. The imagery was collected using the standard integrated camera on a DJI Phantom 4 Pro quadcopter. A Hough transform-based approach for density estimation of crop and weed was used. The detection process began by extracting all plants from the soil background based on visible atmospherically resistant index values, and further discriminated between crop and weed using Hough line transform, followed by connected component analysis. The algorithm development utilized five subsets of image data collected, where overall accuracy was 83%. The algorithm was applied to a different production cotton field in the following year. Overall accuracy remained the same; however, commission error was reduced. The addition of near infrared reflectance could improve accuracies as many errors were due to a lack of “greenness” in plants, which is the primary factor in assigning visible atmospherically resistant index values.