Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360° view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R2 > 0.91 for individual leaf area; R2 > 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D.

A higher minimum (night-time) temperature is considered a greater limiting factor for reduced rice yield than a similar increase in maximum (daytime) temperature. While the physiological impact of high night temperature (HNT) has been studied, the genetic and molecular basis of HNT stress response remains unexplored. We examined the phenotypic variation for mature grain size (length and width) in a diverse set of rice accessions under HNT stress. Genome-wide association analysis identified several HNT-specific loci regulating grain size as well as loci that are common for optimal and HNT stress conditions. A novel locus contributing to grain width under HNT conditions colocalized with Fie1, a component of the FIS-PRC2 complex. Our results suggest that the allelic difference controlling grain width under HNT is a result of differential transcript-level response of Fie1 in grains developing under HNT stress. We present evidence to support the role of Fie1 in grain size regulation by testing overexpression (OE) and knockout mutants under heat stress. The OE mutants were either unaltered or had a positive impact on mature grain size under HNT, while the knockouts exhibited significant grain size reduction under these conditions.

No abstract

24Background 25Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage 26 growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which 27 predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less 28 explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been 29 limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping 30 platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics 31 and mapping of the underlying genes regulating critical yield components. 32 Results 33The major objective of this study is to evaluate post-fertilization development and growth dynamics of 34 inflorescence at high spatial and temporal resolution in rice. For this, we developed the Panicle Imaging 35Platform (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 36 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. 37These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital 38 traits such as voxel count and color intensity. We found that the voxel count of developing panicles is 39 positively correlated with seed number and weight at maturity. The voxel count from developing panicles 40 projected overall volumes that increased during the grain filling phase, wherein quantification of color 41 intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior 42 performance compared to conventional 2D based approaches. 43 Conclusions 44For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of 45 the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-46 throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for 47 crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-48 related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform 49 facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to 50 explore the genetic variation for dynamic inflorescence traits in cereals. 51 52 Keywords 53 plant phenotyping, rice, inflorescence dynamics, 3D imaging, panicle volume, voxel count, panicle 54 maturation, grain filling 55 56 3 Background 57With increasing world population, climatic variability and declining arable land resources, the need to 58 increase global food production is paramount [1][2][3]. Two components that are essential for achieving global 59food security involve precise agronomic management and genetic improvement of major crops such as rice, 60 wheat, and maize. Integral to both components is the developm...

BackgroundRecent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components.ResultsThe major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the Panicle Imaging Platform (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches.ConclusionsFor harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.