High-throughput in Situ 3-D phenotyping of cotton height, leaf area index, and boll distribution
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Hands-on measurements are often used in agronomic studies of cotton (Gossypium hirsutum L.) to determine crop growth, architecture, and maturity; which, in turn, may indicate the effects of genetics, environment, and production practices on crop productivity. Many of these growth characteristics can be tied directly to yield and cotton fiber quality. Traditionally, these methods have been done by hand, requiring extensive labor and often involving destructive sampling methods. The ability of in-field high-throughput phenotyping systems to measure 3-D canopy phenology would benefit plant research and breeding programs by providing a rapid, non-destructive method of determining in-season crop growth and development. In this study, a field-based high-throughput plant phenotyping robotic system mounted with Intel® RealSense™ red-green-blue-distance (RGB-D) cameras was used to generate high-density point clouds in cotton in 2016 and 2017. The three RGB-D sensors, use structured near-Infrared (NIR) laser projectors. Each sensor projected and analyzed a binary search tree to create a depth map while also capturing color images. Two sensors were mounted facing into either side of the canopy, and the third faced downward. Information extracted from the point clouds was used to generate measurements of cotton plant height, leaf area, and cotton boll distribution. Plant height is an important phenotyping trait used for in-season measurements. It is used for the analysis of overall plant growth, is closely related to leaf area index, and has been compared to yield, boll distribution, and maturity in cotton cultivars. Although high-throughput methods of measuring plant height exist, the addition of plant height measurements to 3-D canopy structure measurements would have two added benefits: a decrease in the number of instruments required and the ability to standardize 3-D measurements to a common plane. Height histograms for each plot were extracted from the generated 3-D point clouds using CloudCompare, from which maximum plot height and the 90th percentile value of the plot heights were obtained. The digital measurements were correlated with manual measurements. Pooled seasonal coefficients of determination for each cultivar ranged from .30 -.87, though individual plot coefficients of determination were as high as .99. Measurements of leaf area, were generated both for the entire plant canopy and for individual slices based on 20-mm height segments within the plant canopy. Regression models were obtained from the manual and digital leaf area measurements. These models were then used for the computation of LAI from sensor data. High coefficients of determination between LAI values from sensor and manual measurements for all three cameras were obtained, r2 values ranging from .90 to .97. Coefficients of determination between LAI and dry leaf weight ranged from .89-.94 for all cameras, while those between canopy height and LAI ranged from .88 to .97 for all cameras. Node-by-node boll mapping has been used to determine the effects of irrigation amount and cultivar on cotton boll distribution. Node-specific boll distribution and an overall estimate of boll accumulation using line plots and a vertical box and whisker was used to determine the effects of irrigation and cultivar using a 3-D sensor system to rapidly detect open cotton bolls over a two-year study. Results obtained indicated that both irrigation and cultivar responses were identifiable using the sensor system during both years of this research study. Differences could be observed between the low irrigation and high irrigation plots in terms of boll distribution and boll accumulation for each cultivar. The low irrigation tended to produce bolls more towards the bottom of the plant, while the high irrigation produced bolls towards the top of the plant. Cultivar specific growth characteristics were also observed. The two cultivars, FM2322 and ST4747, showed differences in terms of boll distribution and boll accumulation. ST4747 tended to produce bolls more towards the bottom of the plant, while FM2322 produced bolls towards the top of the plant. Digital and manual measurements were highly correlated with r2 values ranging from .62 to .97, suggesting that the 3-D sensor system can be effectively used for the rapid detection of open cotton bolls. The high-throughput phenotyping system developed in this study measured cotton plant height, leaf area, and cotton boll distribution at the plot level under field conditions, with high accuracy.