Abstract
Plant breeding programs and a wide range of plant science applications would greatly benefit from the development of in-field high throughput phenotyping technologies. In this study, a terrestrial LiDAR-based high throughput phenotyping system was developed. A 2D LiDAR was applied to scan plants from overhead in the field, and an RTK-GPS was used to provide spatial coordinates. Precise 3D models of scanned plants were reconstructed based on the LiDAR and RTK-GPS data. The ground plane of the 3D model was separated by RANSAC algorithm and a Euclidean clustering algorithm was applied to remove noise generated by weeds. After that, clean 3D surface models of cotton plants were obtained, from which three plot-level morphologic traits including canopy height, projected canopy area, and plant volume were derived. Canopy height ranging from 85th percentile to the maximum height were computed based on the histogram of the z coordinate for all measured points; projected canopy area was derived by projecting all points on a ground plane; and a Trapezoidal rule based algorithm was proposed to estimate plant volume. Results of validation experiments showed good agreement between LiDAR measurements and manual measurements for maximum canopy height, projected canopy area, and plant volume, with R2-values of 0.97, 0.97, and 0.98, respectively. The developed system was used to scan the whole field repeatedly over the period from 43 to 109 days after planting. Growth trends and growth rate curves for all three derived morphologic traits were established over the monitoring period for each cultivar. Overall, four different cultivars showed similar growth trends and growth rate patterns. Each cultivar continued to grow until ~88 days after planting, and from then on varied little. However, the actual values were cultivar specific. Correlation analysis between morphologic traits and final yield was conducted over the monitoring period. When considering each cultivar individually, the three traits showed the best correlations with final yield during the period between around 67 and 109 days after planting, with maximum R2-values of up to 0.84, 0.88, and 0.85, respectively. The developed system demonstrated relatively high throughput data collection and analysis.
Highlights
The global population is estimated to approach nine billion by 2050, and demand for food and fiber crops is expected to increase by 60% (Tilman et al, 2011; Gerland et al, 2014)
Morphologic trait information of cotton plants was derived from the reconstructed 3D point clouds
The rectangles indicated by red solid lines for graphs in row 3 of Figure 5 are 2D bounding boxes, and the dashed red lines are the detected boundaries from which projected canopy area (PCA) was derived
Summary
The global population is estimated to approach nine billion by 2050, and demand for food and fiber crops is expected to increase by 60% (Tilman et al, 2011; Gerland et al, 2014). New plant breeding approaches need to be developed to overcome these tremendous challenges. Several studies showed that morphologic traits such as canopy height and leaf area index (LAI) were strongly related to plant species, type of cultivation, plant growth rate, and final yield (Gebbers et al, 2011; Sharma and Ritchie, 2015; Friedli et al, 2016; Sun et al, 2017). Plant growth and yield is dependent upon leaf area development, the average photosynthetic efficiency of all leaves in the plant canopy (Gardner, 1985; Krieg and Sung, 1986), and partitioning of dry matter to the harvested portion of the crop (Earl and Davis, 2003). Plant canopy development should provide some indication of the crop’s capacity for growth and yield
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