Deep Learning Method for Height Estimation of Sorghum in the Field Using LiDAR

Abstract

Automatic tools for plant phenotyping have received increased interest in recent years due to the need to understand the relationship between plant genotype and phenotype. Building upon our previous work, we present a robust, deep learning method to accurately estimate the height of biomass sorghum throughout the entirety of its growing season. We mount a vertically oriented LiDAR sensor onboard an agricultural robot to obtain 3D point clouds of the crop fields. From each of these 3D point clouds, we generate a height contour and density map corresponding to a single row of plants in the field. We then train a multiview neural network in order to estimate plant height. Our method is capable of accurately estimating height from emergence through canopy closure. We extensively validate our algorithm by performing several ground truthing campaigns on biomass sorghum. We have shown our proposed approach to achieve an absolute height estimation error of 7.47% using ground truth data obtained via conventional breeder methods on 2715 plots of sorghum with varying genetic strains and treatments.