Locating Crop Plant Centers From UAV-Based RGB Imagery

Yuhao Chen, Javier Ribera, Christopher Boomsma, Edward Delp; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2030-2037

In this paper we propose a method to find the location of crop plants in Unmanned Aerial Vehicle (UAV) imagery. Finding the location of plants is a crucial step to derive and track phenotypic traits for each plant. We describe some initial work in estimating field crop plant locations. We approach the problem by classifying pixels as a plant center or a non plant center. We use Multiple Instance Learning (MIL) to handle the ambiguity of plant center labeling in training data. The classification results are then post-processed to estimate the exact location of the crop plant. Experimental evaluation is conducted to evaluate the method and the result achieved an overall precision and recall of 66% and 64%, respectively.