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. An important step in this direction is to gain a better understanding of the relationship between genotype and phenotype (Goggin et al., 2015; Großkinsky et al., 2015; Rahaman et al., 2015). However, in-field high throughput phenotyping technologies, which can facilitate automatic measurement of phenotypic traits over the entire growing season, are still considered to be a major bottleneck limiting crop improvement (Furbank and Tester, 2011; Cobb et al., 2013).

Plant morphologic traits can often be used for evaluating plant growth (Hosoi and Omasa, 2012; Taheriazad et al., 2016), which determines plant performance in terms of final crop biomass and yield (Dhondt et al., 2013). 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). Importantly, 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). Thus, plant canopy development should provide some indication of the crop's capacity for growth and yield.

The traditional manual measurement of plant morphologic traits is time consuming, labor intensive, and sometimes destructive. Novel technologies for plant phenotyping in a non-invasive and high throughput manner with high spatial and temporal resolution offer improved efficiency (Furbank and Tester, 2011; Dhondt et al., 2013; Großkinsky et al., 2015). Over the past decade, several non-invasive approaches using sensing technologies were developed for in-field phenotyping (Lin, 2015; Simko et al., 2016). Computer vision was one commonly-used technology. Usually, plant traits were extracted from color (RGB) images. Li et al. (2016) introduced a method for in-field cotton boll detection based on color and texture features using 2D color images. Si et al. (2015) developed a machine vision system to automatically recognize and locate apples; over 89.5% accuracy was achieved. Such 2D image based methods provide potential to conduct phenotypic measurements with a high spatial resolution, but are limited by plant occlusion. In addition, one major challenge with 2D digital image methods is that image quality is significantly affected by highly variable illumination conditions in the field, which limits automatic data processing (Li et al., 2014).

The use of 3D model-based methods for plant phenotyping are receiving increasing attention, as they permit multiple morphologic traits such as canopy height, plant volume, and LAI to be simultaneously extracted (Bietresato et al., 2016; Vazquez-Arellano et al., 2016; Gibbs et al., 2017) while mitigating plant occlusion. Moreover, 3D models have the potential to assist growers to continuously monitor and quantify plant growth and development, as well as plant responses to environmental stresses. A stereo-imaging based 3D reconstruction system was established to capture rape seedling leaf area and plant height (Xiong et al., 2017); two identical RGB cameras were utilized as an imaging unit. The mean error for leaf area and plant height measurements was 3.68 and 6.18%, respectively. The system was put in a well-designed box, in which homogenous illumination was provided. Thuy Tuong et al. (2015) developed a 3D reconstruction system based on 10 digital color cameras that were mounted on a custom structure, and an illumination system was used to enhance the visual texture of plants from all camera viewpoints. The system produced very high quality, dense, and complete point clouds. However, as both systems were designed for indoor use, they would need to be modified for field applications under natural illumination. 3D models can also be produced by time of flight (TOF) cameras; however, similarly to RGB image based methods, data quality would be significantly affected by sunlight under field conditions, which limits in-field applications. In Busemeyer et al. (2013) and Jiang et al. (2016) the TOF cameras were mounted inside an enclosure in order to mitigate the influence of sunlight.

Light detection and ranging (LiDAR) technology provides an alternative approach for 3D plant model reconstruction. LiDAR is a remote sensing technology to measure the distance between the sensor and an object of interest by illuminating the object with a laser and analyzing the TOF. LiDAR may be the best known and most widely used sensor for 3D canopy reconstruction (Deery et al., 2014; Gibbs et al., 2017). A 2D LiDAR collects two dimensional scans in a measured plane, and a 3D model can be obtained by moving the sensor along the perpendicular direction to the scanning plane. Although the spatial resolution of the 3D model produced by LiDAR is not as dense as those obtained by camera-based methods, it is sufficient for the extraction of most plant morphologic traits (Rosell-Polo et al., 2009; Bietresato et al., 2016; Sun et al., 2017). In Deery et al. (2014), a LiDAR (LMS400, SICK AG, Waldkirch, Germany) with a monochromatic red laser light source was used to generate intensity images of several crops including rice, wheat, and maize. It was concluded that LiDAR is a potential alternative to image-based methods for phenotyping morphologic traits at the plot or plant level under field conditions. Moreover, in contrast to image based methods, the LiDAR based method uses its own light source, mitigating problems with highly variable illumination conditions in the field. In addition, LiDAR can be used with a high scanning frequency and a large scanning range (Lin, 2015). Therefore, LiDAR has excellent potential for in-field plant phenotyping. 3D point clouds can also be generated by some other sensors such as triangulation line scanner (Paulus et al., 2014b) and ultrasonic sensing (Llorens et al., 2011). A very dense 3D model can be reconstructed using a triangulation line scanner and morphologic traits at the organ level could be extracted, however the relatively short working distance limits its application for large plants such as cotton.

Growth dynamics of plant morphologic traits provide important information toward determining plant productivity. Tessmer et al. (2013) described a high-throughput phenotyping platform for plant growth modeling and functional analysis (HPGA). Plant growth curves were generated by the platform, which were used to gain a deeper understanding of energy distribution. Friedli et al. (2016) introduced a terrestrial 3D laser scanner-based plant growth monitoring system which could be used for monitoring canopy height growth for different crops under field conditions. Paulus et al. (2014b) monitored the organ-specific growth dynamics of cereal plants with a high precision triangulation line laser scanner by scanning every 2–3 days. Three morphologic traits including leaf area, stem height, and plant volume were measured, allowing quantification of the growth dynamics of the barley plant.

In the present study, three morphologic traits—canopy height, projected canopy area, and plant volume—of cotton plants were derived based on data collected by a 2D LiDAR. Morphologic trait data was collected and growth analysis was conducted. Several improvements were made over previously mentioned studies. A 1-cm accuracy level RTK-GPS was used in this study in order to provide accurate spatial coordinates for LiDAR scans so that a precise 3D surface model could be reconstructed, which is important because the model is the basic dataset for further analysis. In addition, analyses of growth dynamics and correlation of morphologic traits with final yield were conducted over the growing season. The system repeatedly scanned plants over the growing season, permitting analysis of the effects of different cultivars on the growth and final yield of cotton plants.

The overall goal of this work was to develop a high throughput phenotyping system for morphologic traits of cotton plants using LiDAR under field conditions. This LiDAR-based system is one component of our broader effort to develop field-based high throughput phenotyping (HTP) systems, and it complements other image-based sensors such as Kinect V2, thermal camera, and hyperspectral camera. This LiDAR-based system provides accurate morphologic traits in a robust and fast way, and saves storage space and computing resources compared to image-based sensors. Specific objectives were to: (1) develop algorithms to extract multiple morphologic traits—canopy height, projected canopy area, and plant volume—from a 3D point cloud obtained with a 2D LiDAR; (2) conduct 4D monitoring (3D plant reconstructions over time) of the derived plant morphologic traits to detect growth patterns of plants from different cultivars; and (3) explore relationships between derived morphologic traits and final yield.

We have demonstrated that 3D point clouds reconstructed based on data collected by a 2D LiDAR and an RTK-GPS, and associated data processing methodology, accurately estimated specific morphologic traits from cotton plants under field conditions. The precise 3D point cloud, which was the basic dataset for analysis, was critical for the successful extraction of plant morphologic traits (Paproki et al., 2012; Paulus et al., 2014b; Duan et al., 2016). The reconstructed 3D model visually represented plant canopy structures (Figure 5). The CH, PCA, and PV derived by our system were highly correlated (the slopes were close to one) with those measured manually (Figure 6).

The system demonstrated great potential for high-throughput phenotypic analysis. The number of points for each plot varied from 2,000 to 20,000 over the monitoring period, depending on the plant size. The tractor was driven in the field at a speed of around 0.5 m/s for data collection, taking about 6 s to scan one plot that contained 15 plants. Thus, one could anticipate scanning about 600 such plots per hour. The average time consumed for 3D reconstruction and parameter extraction was around 5.27 s per plot so about an hour for the aforementioned 600-plot field. Therefore, both data acquisition and data analysis speeds are suitable for application to field experiments of the sizes used in many breeding programs. The system also has great potential to implement online data analysis with faster computing power in the future, which is particularly useful for large field applications.

The high throughput phenotypic analysis by a LiDAR-based method has several advantages compared to image-based methods. Paproki et al. (2012) conducted cotton plant morphologic trait analysis using an image-based 3D reconstruction method, requiring about 7 min to collect images for each plant and an average processing time of 15 min for 3D reconstruction work of each plant. Additionally, image quality could be significantly affected by highly variable illumination conditions (Nuske et al., 2014), which limited its in-field applications. In contrast, LiDAR is more versatile in a wide range of light conditions since it is equipped with its own light source. For manual operation, ~30 min were required for a typical analysis per plant depending on the size and complexity, and destructive harvests were often required (Paproki et al., 2012).

The high throughput of the proposed system and its non-invasive features permit data acquisition repeatedly over the growing season, opening the door to acquisition of plant growth rate variation, which is valuable for a wide range of applications such as building plant growth models (Tessmer et al., 2013; Weraduwage et al., 2015), exploring factors influencing growth processes (Rahaman et al., 2015; Awlia et al., 2016), genomics-assisted crop breeding (Watanabe et al., 2017), and QTL analysis (Bac-Molenaar et al., 2015; Cui et al., 2017). In this study, all plants in the experimental field were scanned from 43 to 109 DAP. Plant growth curves were generated and correlation between derived parameters and the final yield were explored, which could be used for yield prediction (Sharma et al., 2015) and species identification (Remagnino et al., 2016). Thus, the system described herein not only provides an indication of which LiDAR-derived parameters are most closely associated with yield, but also allows us to define the period during the growing season in which a morphologic trait has the greatest correlation with final yield.

While CH in the current study was highly correlated with within-cultivar yield variation in some instances (i.e., cultivar 4 on later sample dates), it should be noted that correlations between CH and yield for all cultivars combined were substantially lower than those obtained for other derived parameters such as PCA and PV. This is not surprising since the cotton crop exhibits a quadratic response of yield to CH (Sui et al., 2012), where internode elongation must often be controlled using exogenously applied plant growth regulators (PGRs) in real-world production scenarios (Dodds et al., 2010). While CH may not be inherently predictive of genotypic differences in yield, the deployment of high-throughput methods for estimating maximum CH and rate of change in CH could strongly influence cultivar-specific PGR management strategies. Specifically, cultivars with rapid plant height development typically require more aggressive management strategies (Collins, 2013).

PCA and PV were more strongly associated with yield than CH when considered across all cultivars, suggesting these parameters might have greater utility as high-throughput phenotyping tools to identify potential differences in productivity. The impact of canopy development on yield is easily understood when yield is expressed as the product of total intercepted photosynthetically active radiation, radiation use efficiency, and harvest index (Monteith and Moss, 1977; Monteith, 1994; Earl and Davis, 2003; Stöckle and Kemanian, 2009). Within this conceptual framework, the amount of intercepted photosynthetically active radiation during a growing season will be strongly impacted by the length of the growing season and the leaf area available to intercept solar radiation. Thus, PCA may serve as a suitable proxy for leaf area magnitude and persistence throughout the growing season. PV incorporates both CH and PCA, which could potentially be related to both the amount of leaf area available to intercept incoming solar radiation and the three dimensional space available for developing fruit. Both of these considerations are important given the indeterminate nature of cotton. As fruit development progresses, vegetative growth slows, and Constable and Bange (2015) have clearly illustrated that the ability of the cotton crop to attain high yield potential will be influenced by total leaf area and number of fruiting sites available prior to the development of a large fruiting load on the plant. Thus, it is not surprising that PCA and PV were correlated with yield for much of the growing season. Future work should couple the aforementioned measures with high-throughput assessment of fruit development as fruiting dynamics can drastically impact final yield in cotton (Constable and Bange, 2015).

The top view scan setting of LiDAR provided precise 3D surface models of plants; however, organs under the canopy could not be reached due to plant occlusion. This does not reduce the accuracy of CH and PCA measurements, but hinders the estimation of PV by our system, especially when plant structure becomes complex. Multi-view scan is a commonly used method to reduce plant occlusion effects (Paulus et al., 2014b), and in our future studies two side view scans could be added. In addition, algorithms such as plant shape models (Pastrana and Rath, 2013) have been proposed to overcome plant occlusion effects. However, when plant structure complexity increases, especially at the mature stage with a greatly increased number and size of leaves and stems, occlusion effects would still be present. Plant occlusion remains a challenge for plant phenotyping, especially under field conditions with limited inter-row spacing (Paproki et al., 2012; Paulus et al., 2014a). Wind is another factor which could affect the accuracy of the derived traits since it might result in blurred point clouds. Therefore, calm weather conditions are best for data collection. Based on the CH validation results, our system has a certain robustness against wind influence; wind speed under 3 m/s was feasible. Another limitation was that the tractor speed was restricted by the scanning frequency of the LiDAR (Sun et al., 2017), limiting throughput. While the present system is already compatible with iterative study of thousands of plots, 3D LiDAR technology may greatly reduce this limitation (Weiss and Biber, 2011).

Precise 3D surface models were reconstructed by the high-throughput phenotyping system developed in this study under field conditions. Multiple morphologic traits at the plot level including plant height, projected canopy area, and plant volume were extracted simultaneously. The system could be used to scan the field repeatedly due to its relatively high data collection and processing capability, which was particularly useful for large field applications. The measured morphologic traits were most highly correlated with final yield during the period between around 67 and 109 DAP. Projected canopy area and plant volume were more closely correlated than plant height to final yield. Future work will focus on involving other sensor data to extract more phenotypic traits from the 3D point cloud. Although this system was solely tested on cotton plants, it is expected to be applicable for use with other crops such as wheat, rice, and soybeans.

SS, CL, and AP: conceived the idea and designed the experiments; AP and PC: contributed to the preparation of materials and equipment; SS, YJ, RX, and JR: conducted the experiments; SS, YJ, and RX: analyzed the data; SS, CL, AP, JS, and PC: interpreted results and wrote the paper; All authors read and approved the final manuscript.