Estimating Phenotypic Traits From UAV Based RGB Imagery
This work addresses the need for efficient phenotyping in agriculture, though it appears incremental as it builds on existing imagery-based approaches.
The paper tackles the problem of labor-intensive plant phenotyping by developing methods to estimate sorghum traits from UAV-based RGB imagery, resulting in techniques for generating orthophoto mosaics, estimating leaf counts, and locating individual plants.
In many agricultural applications one wants to characterize physical properties of plants and use the measurements to predict, for example biomass and environmental influence. This process is known as phenotyping. Traditional collection of phenotypic information is labor-intensive and time-consuming. Use of imagery is becoming popular for phenotyping. In this paper, we present methods to estimate traits of sorghum plants from RBG cameras on board of an unmanned aerial vehicle (UAV). The position and orientation of the imagery together with the coordinates of sparse points along the area of interest are derived through a new triangulation method. A rectified orthophoto mosaic is then generated from the imagery. The number of leaves is estimated and a model-based method to analyze the leaf morphology for leaf segmentation is proposed. We present a statistical model to find the location of each individual sorghum plant.