Plant Identification in Mosaicked Crop Row Images for Automatic Emerged Corn Plant Spacing Measurement

Abstract

Image processing algorithms for individual corn plant and plant stem center identification were developed. These algorithms were applied to mosaicked crop row image for automatically measuring corn plant spacing at early growth stages. These algorithms utilized multiple sources of information for corn plant detection and plant center location estimation including plant color, plant morphological features, and the crop row centerline. The algorithm was tested over two 41 m (134.5 ft) long corn rows using video acquired two times in both directions. The system had a mean plant misidentification ratio of 3.7%. When compared with manual plant spacing measurements, the system achieved an overall spacing error (RMSE) of 1.7 cm and an overall R2 of 0.96 between manual plant spacing measurement and the system estimates. The developed image processing algorithms were effective in automated corn plant spacing measurement at early growth stages. Interplant spacing errors were mainly due to crop damage and sampling platform vibration that caused mosaicking errors.