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System and Algorithm Development of Automatic Corn Plant Identification Using Laser Line-scan Technique

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2012 Dallas, Texas, July 29 - August 1, 2012  121338437.(doi:10.13031/2013.42459)
Authors:   Yeyin Shi, Ning Wang, Randal Kevin Taylor, William R Raun, James A Hardin
Keywords:   Plant spacing, plant population, laser scanner, data clustering, in-field variability

Identifying corn plant location and/or spacing is important for predicting yield potential and making decisions for in-season nitrogen application rate. In this study, an automatic corn stalk identification system based on a laser line-scan technique was developed to measure stalk locations during corn mid growth stages. A laser line-scan technique is advantageous in this application because the line-scan data sets taken from various points of view of a plant stalk results in less interference and higher probability of plant recognition. Data were collected for two 10 m corn rows at growth stage V8 and V10 using a mobile test platform in 2011. Each potential stalk cluster was identified in a scan and registered with the same stalks in previous scans. The final location of a stalk was the average of the measured locations in all scans. The current system setup with data processing algorithms achieved 24.0% and 10.0% of mean total errors in plant counting at V8 and V10 growth stages, respectively. The root mean squared error (RMSE) between system measured plant locations and manually measured ones were 2.3 cm and 2.6 cm at V8 and V10 growth stages, respectively. The interplant spacing measured by the developed system had a good correlation with the manual measurement with an R2 of 0.962 and 0.951 for V8 and V10 growth stages respectively. This system can be ultimately integrated in a variable-rate-spraying system to improve real-time, high spatial resolution variable rate nitrogen applications.

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