electronic imaging | 2019
In situ width estimation of biofuel plant stems
Abstract
Efficient plant phenotyping methods are necessary in order to accelerate the development of high yield biofuel crops. Manual measurement of plant phenotypes, such as width, is slow and error-prone. We propose a novel approach to estimating the width of corn and sorghum stems from color and depth images obtained by mounting a camera on a robot which traverses through plots of plants. We use deep learning to detect individual stems and employ filters, morphological operations, and Random Sample Consensus to model the boundary of each stem and estimate the pixel width and metric width of each stem. This approach results in 13.5% absolute error in the pixel domain on corn and 13.2% metric absolute error on phantom sorghum.