Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat

Abstract

Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability and the ability to analyse big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack LiDAR device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilisation in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We therefore believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.

Field-measured canopy height may not be as accurate and heritable as believed – Evidence from advanced 3D sensing

“…LiDAR has many advantages, including 1) strong penetration ability that can characterize the inner structure of the canopy, 2) real and direct 3D characterization of an object without a complicated reconstruction process, and 3) insensitive to illumination. According to different mounting platforms, LiDAR systems used for crop height measurement mainly include terrestrial laser scanning (TLS) (Tilly et al, 2014b;Guo et al, 2019), backpack laser scanning (BLS) (Zhu et al, 2021), gantry laser scanning (GLS) (Li et al, 2022c;Sun et al, 2022), and unmanned-aerial-vehicle laser scanning (ULS) (Zhou et al, 2020;Luo et al, 2021;Sofonia et al, 2019). In contrast to the active LiDAR sensing technologies, passive sensing-based methods (e.g., Multi-view images) can also measure 3D structure through methods like structure from motion (SFM) (Holman et al, 2016;Malambo et al, 2018).…”

Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera

“…LiDAR can record the spatial coordinates (XYZ) and intensity information of a target by measuring the distance between the sensor and the target with a laser and analyzing the time of flight (ToF) (Sun et al, 2018). Although the spatial resolution of the 3D model produced by LiDAR is not as dense as those obtained by image-based methods, it is sufficient for extracting most plant morphologic traits (Zhu et al, 2021). As an active sensor, LiDAR can operate regardless of illumination conditions, and 3D lasers were considered to have lower throughput for 1-2 minutes were required to collect data at each plot (Virlet et al, 2017).…”

AirMeasurer: open‐source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice

“…duckweed) compared with dryland crops (Ogawa et al ., 2021). Moreover, few research groups have the resources to process large‐scale aerial images, or to develop complex algorithms to address problems in automated trait analysis (Roitsch et al ., 2019; Zhu et al ., 2021). Hence, along with the development of open‐source computer vision, machine learning and data science libraries (Howse, 2013; Virtanen et al ., 2020), open solutions will be valuable to equip plant researchers with new toolkits to study complicated crops.…”