Corn Seedling Monitoring Using 3-D Point Cloud Data From Terrestrial Laser Scanning and Registered Camera Data

Abstract

In precision farming, the separation of crops from soil is crucial for monitoring growth and fertilization. In this letter, a novel method is proposed for the accurate detection of corn seedlings from cropland by combining terrestrial laser scanning (TLS) and camera data. First, a piecewise linear interpolation method was used to eliminate the effect of distance on the TLS intensity data for more accurate intensity features of scanned targets. Second, the point cloud and camera data were registered to obtain the true color of each point in the point cloud. Third, we used a random forest algorithm to separate corn seedlings from soil by combining the geometric features from the TLS data with the radiometric features including the corrected intensity and RGB values derived from the TLS and camera data. To evaluate the proposed method, a case study was conducted by using a commercial TLS sensor with an embedded camera. The results demonstrated that corn seedlings can be separated from soil with an accuracy of 98.8% by using both the geometric and radiometric features, which is significantly higher than that by using any one of the two kinds of features.