In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR

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.

Acquiring Plant Features with Optical Sensing Devices in an Organic Strip-Cropping System

“…The use of a weighted sum that assigns a quantifiable value that can be statistically evaluated contributes to the more accurate separation between soil and vegetation that is especially needed in lower crops. Even though the RANSAC algorithm has been applied successfully in other lower vegetation with similar dimensions like cotton [33], the method presented in this paper improves that process, at the cost of needing higher computational power. The volume of the resulting vegetation point cloud obtained in Reference [33] is estimated using a Trapezoidal rule based algorithm, which might be a computationally cheaper alternative to the highly accurate convex hull calculations used in orchard scans, thereby providing a trade-off in computational power.…”

High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture

“…In another study, a similar high-clearance tractor was used to mount a LiDAR scanner and an RTK-GPS for building 3D models of cotton plants. From the obtained data, canopy height, canopy area and plant volume were estimated [57]. In yet another study, a phenotyping vehicle called "GPhenoVision" was developed using a high-clearance tractor, and mounted with a stereo RGB camera, thermal camera, hyperspectral camera and an RTK-GPS [58].…”

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

“…() to measure the epoxidation state of xanthophyll pigments as an indirect measure of NPQ. High‐throughput phenotyping of photosynthesis using such imaging devices should be relatively easy to include in phenotyping systems like those that support cameras which move above the crop canopy (Kirchgessner et al ., ; Sun et al ., ), and may provide a solution for measuring specific traits for field‐grown crops.…”