Efficient in‐field plant phenomics for row‐crops with an autonomous ground vehicle. Issue 6 (29th May 2017)

• Record Type: Journal Article
• Authors: Underwood, James; Wendel, Alexander; Schofield, Brooke; McMurray, Larn; Kimber, Rohan
• Other Names: Ball David guestEditor.; Upcroft Ben guestEditor.; van Henten Eldert guestEditor.; van den Hengel Anton guestEditor.; Tokekar Pratap guestEditor.; Das Jnaneshwar guestEditor.
• Is Part Of: Journal of field robotics. Volume 34:Issue 6(2017)
• Page Start: 1061
• Page End: 1083
• Publication Date: 2017-05-29
• Subjects: agriculture -- hyperspectral and lidar sensing -- plant phenomics -- row-crop phenotyping -- terrestrial robotics; Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.21728
• Languages: English
• ISSNs: 1556-4959

Abstract

Abstract: The scientific areas of plant genomics and phenomics are capable of improving plant productivity, yet they are limited by the manual labor that is currently required to perform in‐field measurement, and a lack of technology for measuring the physical performance of crops growing in the field. A variety of sensor technology has the potential to efficiently measure plant characteristics that are related to production. Recent advances have also shown that autonomous airborne and manually driven ground‐based sensor platforms provide practical mechanisms for deploying the sensors in the field. This paper advances the state‐of‐the‐art by developing and rigorously testing an efficient system for high throughput in‐field agricultural row‐crop phenotyping. The system comprises an autonomous unmanned ground‐vehicle robot for data acquisition and an efficient data post‐processing framework to provide phenotype information over large‐scale real‐world plant‐science trials. Experiments were performed at three trial locations at two different times of year, resulting in a total traversal of 43.8 km to scan 7.24 hectares and 2423 plots (including repeated scans). The height and canopy closure data were found to be highly repeatable ( r 2 = 1.00 N = 280, r 2 = 0.99 N = 280, respectively) and accurate with respect to manually gathered field data ( r 2 = 0.95 N = 470, r 2 = 0.91 N = 361, respectively), yet more objective and less‐reliant on human skill and experience. The system was found to be a more labor‐efficient mechanism for gathering data, which compares favorably to current standard manual practices.