Authors

Abstract

The acquisition of traditional wheat ear phenotypic parameters is labour intensive and subjective, and some trait parameters are difficult to measure, which greatly limits the progress of wheat ear research. To obtain the phenotypic parameters of wheat ears in batches at a low cost, this paper proposed a convenient and accurate method for extracting phenotypic parameters of wheat ears. First, three improvement directions were proposed based on the Mask-RCNN model. 1) To extract the multiscale features of wheat ears, a hierarchical residual link was constructed in a single residual block of the backbone network ResNet101 to obtain information on different sizes of receptive fields. 2) The FPN was improved to increase the recognition accuracy of wheat ear edges through multiple two-way information flow sampling. 3) The mask evaluation mechanism was improved, specific network blocks were used to learn and predict the quality of the mask, and the detection of wheat ears and grains was performed by precise segmentation; an automatic extraction algorithm was designed for wheat ear phenotypic parameters based on the segmentation results to extract 22 phenotypic parameters. The experiments showed that the improved Mask-RCNN was superior to the existing model in the segmentation accuracy of wheat ears and grains; the parameters of wheat ear length, width, and number of grains extracted by the automatic extraction algorithm were close to the manual measurement values. This research meets the demand for automatic extraction of wheat ear phenotype data for large-scale quality testing and commercial breeding and has strong practicability.

Abstract in Chinese

传统麦穗表型参数获取劳动强度大，主观性强，且部分性状参数难以测量，很大程度上限制了麦穗研究的进展。为了能用低成本批量获取麦穗的表型参数，本文提出一种便捷且精准的麦穗表型特征参数提取方案。首先基于Mask-RCNN模型提出三种改进方向，1）为了提取麦穗多尺度特征，在主干网络ResNet101的单个残差块内构建分层残差类链接以获取不同大小感受野的信息；2）改进FPN金字塔网络，通过多次双向信息流采样提高麦穗边缘的识别精度；3）增加掩码评价机制，采用特定的网络块来学习和预测掩码的质量，最终实现对麦穗和籽粒的精准分割。然后针对分割结果设计一种麦穗表型参数自动提取算法，提取包含麦穗的成熟度信息、颜色、形状、空间等22个表型特征的参数。实验证明本文改进后的Mask-RCNN在麦穗与籽粒的分割精度上优于现有模型；麦穗表型参数自动提取算法提取的麦穗长、宽、籽粒个数的参数接近人工测量值，满足大规模质量检测和商业化育种对麦穗表型数据自动化提取的需求，具有较强的实用性。