Chenglong Huang

Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice

Even as the study of plant genomics rapidly develops through the use of high-throughput sequencing techniques, traditional plant phenotyping lags far behind. Here we develop a high-throughput rice phenotyping facility (HRPF) to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period. Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1. Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information. The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.

A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice

The evaluation of yield-related traits is an essential step in rice breeding, genetic research and functional genomics research. A new, automatic, and labor-free facility to automatically thresh rice panicles, evaluate rice yield traits, and subsequently pack filled spikelets is presented in this paper. Tests showed that the facility was capable of evaluating yield-related traits with a mean absolute percentage error of less than 5% and an efficiency of 1440 plants per continuous 24 h workday.

Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer

Highlight A combination of high-throughput leaf phenotyping and genome-wide association analysis provides valuable insights into the genetic basis of rice leaf traits.

High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth

Combining high-throughput phenotyping and large-scale QTL mapping dissects the dynamic genetic architecture of maize development by using a RIL population. With increasing demand for novel traits in crop breeding, the plant research community faces the challenge of quantitatively analyzing the structure and function of large numbers of plants. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In this study, we quantified 106 traits from a maize (Zea mays) recombinant inbred line population (n = 167) across 16 developmental stages using the automatic phenotyping platform. Quantitative trait locus (QTL) mapping with a high-density genetic linkage map, including 2,496 recombinant bins, was used to uncover the genetic basis of these complex agronomic traits, and 988 QTLs have been identified for all investigated traits, including three QTL hotspots. Biomass accumulation and final yield were predicted using a combination of dissected traits in the early growth stage. These results reveal the dynamic genetic architecture of maize plant growth and enhance ideotype-based maize breeding and prediction.

Development of a whole-feeding and automatic rice thresher for single plant ☆

Abstract Threshing is an essential pretreatment in rice yield-related traits evaluation and rice thresher is an important study of agricultural machinery automation. However, traditional threshers aim at threshing filled grains with simple function, which are inapplicable to high-precision and automatic yield-related traits evaluation of rice. And the conventional threshing method for the traits evaluation is still manual. To improve it, we developed a whole-feeding and automatic rice thresher for single plant. The thresher adopted a hierarchical multi-roller rolling method to thresh filled and unfilled grains respectively. A fish scale sieve plate was designed to separate the grains from the straw and the whole system was controlled by Programmable Logic Controller (PLC) automatically. To evaluate the threshing system, two batches of rice plants were tested, and the results showed that the thresher had the advantages of high precision, low breakage and no residue. Thus, the thresher provides strong support for high-precision and automatic yield-related traits evaluation of rice.

High-Throughput Estimation of Yield for Individual Rice Plant Using Multi-angle RGB Imaging

Modern breeding technologies are capable of producing hundreds of new varieties daily, so fast, simple and effective methods for screening valuable candidate plant materials are urgently needed. Final yield is a significant agricultural trait in rice breeding. In the screening and evaluation of the rice varieties, measuring and evaluating rice yield is essential. Conventional means of measuring rice yield mainly depend on manual determination, which is tedious, labor-intensive, subjective and error-prone, especially when large-scale plants were to be investigated. This paper presented an in vivo, automatic and high-throughput method to estimate the yield of individual pot-grown rice plant using multi-angle RGB imaging and image analysis. In this work, we demonstrated a new idea of estimating rice yield from projected panicle area, projected area of leaf and stem and fractal dimension. 5-fold cross validation showed that the predictive error was 7.45%. The constructed model achieved promising results on rice plants grown both in-door and out-door. The presented work has the potential of accelerating yield estimation and would be a promising impetus for plant phenomics.

Development of an Automatic Control System for Pot-Grown Rice Inspection Based on Programmable Logic Controller

Rice improvement breeding is one of the most important research fields in China. With the development of modern rice breeding technology, hundreds to thousands of new varieties are produced daily, creating the impetus for rapid plant phenotyping evaluation. However, traditional measurement is inefficiency, contact-interferential, and lack-objectivity. Thus a high-throughput and automatic extraction system for rice plant is imperative. In this article we developed a rice phenotyping automatic extraction system and designed the automatic control for the system based on programmable logic controller (PLC). Subsequently, the prototype was test under industrial conditions continuous 24 h workdays and the error probability was less than 0.01%. In sum, base on PLC, we provide an efficient and stable automatic control system for pot-grown rice phenotyping inspection.

An integrated hyperspectral imaging and genome-wide association analysis platform provides spectral and genetic insights into the natural variation in rice

With progress of genetic sequencing technology, plant genomics has experienced rapid development and subsequently triggered the progress of plant phenomics. In this study, a high-throughput hyperspectral imaging system (HHIS) was developed to obtain 1,540 hyperspectral indices at whole-plant level during tillering, heading, and ripening stages. These indices were used to quantify traditional agronomic traits and to explore genetic variation. We performed genome-wide association study (GWAS) of these indices and traditional agronomic traits in a global rice collection of 529 accessions. With the genome-level suggestive P-value threshold, 989 loci were identified. Of the 1,540 indices, we detected 502 significant indices (designated as hyper-traits) that exhibited phenotypic and genetic relationship with traditional agronomic traits and had high heritability. Many hyper-trait-associated loci could not be detected using traditional agronomic traits. For example, we identified a candidate gene controlling chlorophyll content (Chl). This gene, which was not identified based on Chl, was significantly associated with a chlorophyll-related hyper-trait in GWAS and was demonstrated to control Chl. Moreover, our study demonstrates that red edge (680–760 nm) is vital for rice research for phenotypic and genetic insights. Thus, combination of HHIS and GWAS provides a novel platform for dissection of complex traits and for crop breeding.

Rapid Identification of Rice Varieties by Grain Shape and Yield-Related Features Combined with Multi-class SVM

Rice is the major food of approximately half world population and thousands of rice varieties are planted in the world. The identification of rice varieties is of great significance, especially to the breeders. In this study, a feasible method for rapid identification of rice varieties was developed. For each rice variety, rice grains per plant were imaged and analyzed to acquire grain shape features and a weighing device was used to obtain the yield-related parameters. Then, a Support Vector Machine (SVM) classifier was employed to discriminate the rice varieties by these features. The average accuracy for the grain traits extraction is 98.41 %, and the average accuracy for the SVM classifier is 79.74 % by using cross validation. The results demonstrated that this method could yield an accurate identification of rice varieties and could be integrated into new knowledge in developing computer vision systems used in automated rice-evaluated system.

Accurate Inference of Rice Biomass Based on Support Vector Machine

Biomass is an important phenotypic trait in plant growth analysis. In this study, we established and compared 8 models for measuring aboveground biomass of 402 rice varieties. Partial least squares (PLS) regression and all subsets regression (ASR) were carried out to determine the effective predictors. Then, 6 models were developed based on support vector regression (SVR). The kernel function used in this study was radial basis function (RBF). Three different optimization methods, Genetic Algorithm (GA) K-fold Cross Validation (K-CV), and Particle Swarm Optimization (PSO), were applied to optimize the penalty error C and RBF \( \upgamma \). We also compared SVR models with models based on PLS regression and ASR. The result showed the model in combination of ASR, GA optimization and SVR outperformed other models with coefficient of determination (R2) of 0.85 for the 268 varieties in the training set and 0.79 for the 134 varieties in the testing set, respectively. This paper extends the application of SVR and intelligent algorithm in measurement of cereal biomass and has the potential of promoting the accuracy of biomass measurement for different varieties.