Robust Plant Localization and Phenotyping in Dense 3D Point Clouds for Precision Agriculture

The determination of a crop's growth-stage is critical information for precision agriculture. Estimates of the growth-stage are used to guide irrigation and the application of agrochemicals. Of particular importance is the use of fertilizers, however, growth-stage estimates may also suggest further investigation of potential crop infections and infestations. Traditionally, the growth-stage is based upon a manual random sample of a very small number of plants that are then analyzed to produce an estimate for the entire crop (up to thousands of acres). In order to increase the sample size (and thus accuracy) and to enable precision agriculture to address non-uniform crop development across a field, we present an analysis methodology that facilitates the automated growth-stage analysis of dense point clouds that are derived from drone imagery. Our method utilizes a standard camera drone and does not use specialized sensors or geo-spatial tagging. We propose a multi-stage unsupervised method, which provides information about the individual plant locations in a field plot with a high probability. The method also produces a measure of individual plant heights, which along with their location are critical for later growth-stage estimation and necessary for robotic precision application. We confirm our method's efficacy with experimental results on corn fields in Minnesota.