Plant density estimation during the emergence phase is critical for early-season decision making. Estimation of both cropand 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 todevelop a set of algorithms that accurately estimated the crop and weed density at emergence from sUAS imagery, andto do so using methods which were operationally feasible on production-field scale. The areas of interest wereMississippi cotton fields, where weeds were present. The imagery was collected using the standard integrated camera ona 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 resistantindex values, and further discriminated between crop and weed using Hough line transform, followed by connectedcomponent analysis. The algorithm development utilized five subsets of image data collected, where overall accuracywas 83%. The algorithm was applied to a different production cotton field in the following year. Overall accuracyremained the same; however, commission error was reduced. The addition of near infrared reflectance could improveaccuracies as many errors were due to a lack of “greenness” in plants, which is the primary factor in assigning visibleatmospherically resistant index values.
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