The average measuring efficiency was approximately 40 s per sample, which was approximately twenty times more efficient than manual measurement. To evaluate the system accuracy and efficiency, 504 panicle samples were tested, and the total spikelet measurement error decreased from 11.44 to 2.99% with threshed panicle compensation. Additionally, AI cloud computing was adopted, which dramatically reduced the system cost and improved flexibility. To improve the threshed panicle detection accuracy, the Region of Interest Align, Convolution Batch normalization activation with Leaky Relu module, Squeeze-and-Excitation unit, and optimal anchor size have been adopted to optimize the Faster-RCNN architecture, termed ‘TPanicle-RCNN,’ and the new model achieved F1 score 0.929 with an increase of 0.044, which was robust to indica and japonica varieties. To solve these problems, a novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy. The conventional method for rice yield-related trait evaluation faces the problems of rice threshing difficulties, measurement process complexity, and low efficiency. High-throughput phenotyping of yield-related traits is meaningful and necessary for rice breeding and genetic study. This methodology could be scaled‐up to efficiently phenotype large populations and provide the basis for understanding panicle architecture and grain morphology in oat. Our results show a highly efficient and powerful strategy to study panicle architecture in oat. 05 among genotypic means for all panicle architecture traits was reached with the use of six panicles per replication. 80 to detect significant differences at an alpha of. The heritability of all panicle traits was high between. Panicle length, width, aspect ratio, compactness, and digital biomass were obtained from the images. Ten panicles per experimental unit were harvested and evaluated with image analysis using MATLAB. A total of 48 genotypes were evaluated in four field experiments with three true replications in two locations and two years. Specifically, the goals were to create and deploy the foundations for a high‐throughput (HTP) methodology to phenotype oat panicles and to determine the optimum number of panicles needed to detect differences between genotypic means for oat panicle traits with high power. The aim of this research was to propose an image‐based strategy and methodology to evaluate oat panicles. Panicle size and architecture are traits of major relevance for oat production as they play a key role in conditioning the potential number of grains per spikelet and in consequence grain shape, size, and distribution. It can be concluded the architectural traits like rachis length, number of primary secondary branches, and secondary branches and length of primary branches were the most important in improving panicle architecture and crop yield. The increase in number of panicle rachis length, number of primary and secondary branches and length of primary branches resulted in increased spikelet number per panicle and grain yield. The correlation between spikelet number and other variables related to number of primary branches was higher than correlation with variables related to length of primary branch. Correlation analysis revealed significant (p=0.05) and positive correlation of grain yield with architecture traits i.e., number of primary branches (r=0.88), length of primary branches (r=0.89), number of secondary branches(r=0.69), number of nodes (r=84). Four different types of panicle size-irregular, conical, diamond like and pyramid shaped panicle were identified in 42 genotypes. Among genotypes, the highest yield was found in high yielding late variety Sampurna (6.1t/ha) with highest number of both primary (16.33) and secondary branches (53.33). The graphical user interface used image analysis tool Panicle Trait Phenotyping (PTRAP) was used to record architecture and yield related traits. The experiment was conducted in randomized complete block design with three replications and forty-two treatments. An experiment was conducted with the objective of evaluating panicle architecture traits of different genotypes of rice in relation to yield during June to November 2020.
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