Improvement of a ground-LiDAR-based corn plant population and spacing measurement system

Abstract

The variability of corn plant location and within-row spacing has been demonstrated to have a significant correlation with grain and biomass yield. They are included in many yield prediction models which are used to guide mid-season variable-rate fertilizer applications. A prototype sensing system was developed to automatically measure corn plant location and spacing on-the-go based on ground LiDAR technology. The system travelled along crop rows with a ground LiDAR sensor scanning at the bottom section of each corn plant. The possibility of corn stalk identification was increased because each stalk appeared in multiple scans from various view angles of the sensor. The first version of the prototyping system was developed earlier and resulted in a relatively low detecting accuracy. In this paper, an improved version of the prototyping system was presented with substantial additional field evaluation results. The system was improved in terms of the data acquisition platform and the data processing algorithms, specifically, the scan registration and stalk recognition procedures to reduce the misidentification errors. Additional field evaluation was conducted on 200 plants at their V8 growth stage. A total plant counting error of 5.5% and a 1.9cm of root-mean-squared error (RMSE) in spacing measurement were achieved between the sensor measurements and the manually measured ground truth data. The new data processing algorithm was also tested on the data collected with the first version system. The false positive plant counting error was reduced from 24.0% with the first version system to 14.0% with the improved algorithms.