Novel digital features feature discriminate between drought resistant and drought sensitive rice under controlled and field conditions

Dynamic quantification of drought response is a key issue both for variety selection and for functional genetic study of rice drought resistance. Traditional assessment of drought resistance traits, such as stay-green and leaf-rolling, has utilized manual measurements, that are often subjective, error-prone, poorly quantified and time consuming. To relieve this phenotyping bottleneck, we demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions. Firstly, RGB images of individual rice plants at different growth points were analyzed to derive 4 features that were influenced by imposition of drought. These include a feature related to the ability to stay green, which we termed greenness plant area ratio (GPAR) and 3 shape descriptors [total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR) and total plant area/convex hull area ratio (TCR)]. Experiments showed that these 4 features were capable of discriminating reliably between drought resistant and drought sensitive accessions, and dynamically quantifying the drought response under controlled conditions across time (at either daily or half hourly time intervals). We compared the 3 shape descriptors and concluded that PAR was more robust and sensitive to leaf-rolling than the other shape descriptors. In addition, PAR and GPAR proved to be effective in quantification of drought response in the field. Moreover, the values obtained in field experiments using the collection of rice varieties were correlated with those derived from pot-based experiments. The general applicability of the algorithms is demonstrated by their ability to probe archival Miscanthus data previously collected on an independent platform. In conclusion, this image-based technology is robust providing a platform-independent tool for quantifying drought response that should be of general utility for breeding and functional genomics in future

Novel digital features feature discriminate between drought resistant and drought sensitive rice under controlled and field conditions

Dynamic quantification of drought response is a key issue both for variety selection and for functional genetic study of rice drought resistance. Traditional assessment of drought resistance traits, such as stay-green and leaf-rolling, has utilized manual measurements, that are often subjective, error-prone, poorly quantified and time consuming. To relieve this phenotyping bottleneck, we demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions. Firstly, RGB images of individual rice plants at different growth points were analyzed to derive 4 features that were influenced by imposition of drought. These include a feature related to the ability to stay green, which we termed greenness plant area ratio (GPAR) and 3 shape descriptors [total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR) and total plant area/convex hull area ratio (TCR)]. Experiments showed that these 4 features were capable of discriminating reliably between drought resistant and drought sensitive accessions, and dynamically quantifying the drought response under controlled conditions across time (at either daily or half hourly time intervals). We compared the 3 shape descriptors and concluded that PAR was more robust and sensitive to leaf-rolling than the other shape descriptors. In addition, PAR and GPAR proved to be effective in quantification of drought response in the field. Moreover, the values obtained in field experiments using the collection of rice varieties were correlated with those derived from pot-based experiments. The general applicability of the algorithms is demonstrated by their ability to probe archival Miscanthus data previously collected on an independent platform. In conclusion, this image-based technology is robust providing a platform-independent tool for quantifying drought response that should be of general utility for breeding and functional genomics in future

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

International audienceRice 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

Characterization of complete mitochondrial genome of Polyura narcaeus (Lepidoptera: Nymphalidae: Charaxinae)

The complete mitochondrial genome of Polyura narcaeus (Hewitson, 1854) (Lepidoptera: Nymphalidae: Charaxinae) was sequenced in the study. The circular genome is 15,319 bp in size and includes 13 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes and a non-coding AT-rich region. The base composition of the whole mitogenome is 39.15% A, 42.08% T, 11.18% C and 7.59% G, showing a strong AT bias. The characteristics of encoding PCGs, rRNAs and tRNAs, as well as the non-coding intergenic spacers and overlapping sequences are nearly the same with other known butterflies. The AT-rich region also contains several features characteristic of the typical butterflies. Phylogenetic analysis distinctly showed that the family Nymphalidae was a monophyletic group, and that the newly determined Polyura narcaeus of this study was firstly sister to Polyura nepenthes, then they were clustered with Polyura arja

Accurate Inference of Rice Biomass Based on Support Vector Machine

International audienceBiomass 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 γ. 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

Determination of rice panicle numbers during heading by multi-angle imaging

Plant phenomics has the potential to accelerate progress in understanding gene functions and environmental responses. Progress has been made in automating high-throughput plant phenotyping. However, few studies have investigated automated rice panicle counting. This paper describes a novel method for automatically and nonintrusively determining rice panicle numbers during the full heading stage by analyzing color images of rice plants taken from multiple angles. Pot-grown rice plants were transferred via an industrial conveyer to an imaging chamber. Color images from different angles were automatically acquired as a turntable rotated the plant. The images were then analyzed and the panicle number of each plant was determined. The image analysis pipeline consisted of extracting the i2 plane from the original color image, segmenting the image, discriminating the panicles from the rest of the plant using an artificial neural network, and calculating the panicle number in the current image. The panicle number of the plant was taken as the maximum of the panicle numbers extracted from all 12 multi-angle images. A total of 105 rice plants during the full heading stage were examined to test the performance of the method. The mean absolute error of the manual and automatic count was 0.5, with 95.3% of the plants yielding absolute errors within ± 1. The method will be useful for evaluating rice panicles and will serve as an important supplementary method for high-throughput rice phenotyping

High-throughput volumetric reconstruction for 3D wheat plant architecture studies

For many tiller crops, the plant architecture (PA), including the plant fresh weight, plant height, number of tillers, tiller angle and stem diameter, significantly affects the grain yield. In this study, we propose a method based on volumetric reconstruction for high-throughput three-dimensional (3D) wheat PA studies. The proposed methodology involves plant volumetric reconstruction from multiple images, plant model processing and phenotypic parameter estimation and analysis. This study was performed on 80 Triticum aestivum plants, and the results were analyzed. Comparing the automated measurements with manual measurements, the mean absolute percentage error (MAPE) in the plant height and the plant fresh weight was 2.71% (1.08cm with an average plant height of 40.07cm) and 10.06% (1.41g with an average plant fresh weight of 14.06g), respectively. The root mean square error (RMSE) was 1.37cm and 1.79g for the plant height and plant fresh weight, respectively. The correlation coefficients were 0.95 and 0.96 for the plant height and plant fresh weight, respectively. Additionally, the proposed methodology, including plant reconstruction, model processing and trait extraction, required only approximately 20s on average per plant using parallel computing on a graphics processing unit (GPU), demonstrating that the methodology would be valuable for a high-throughput phenotyping platform

Temperature gradient zoning of steel beams without paving layers in China

The solar temperature field and its temperature effects on the straddle monorail tourist transportation system (SMTTS) significantly affect its mechanical performance. To investigate the distribution law of the daylight temperature field of the unpaved steel beams, the daylight temperature fields of box-type and I-sectional steel beams in different regions were measured and simulated. With the simulation results, the vertical temperature gradient curves of the unpaved steel girders were determined. Afterward, with the historical meteorological data as variables, the parameters of the extreme value model of generalized extreme value (GEV) distribution were obtained, and the representative values of temperature differences in China, based on the recurrence period, were analyzed. By analyzing the temperature difference values in 34 cities in China, an empirical prediction formula for the representative temperature difference value with three parameters was proposed, and the temperature gradient partition was established. The results indicated that the vertical temperature gradients of closed-section steel box girders are about 2–3 °C greater than that of open-section I-beams. Moreover, there are apparent geographical differences in the temperature difference values of steel girders