Abstract:

[Objective] Wheat serves as the primary source of dietary carbohydrates for the human population, supplying 20% of the required caloric intake. Currently, the primary objective of wheat breeding is to develop wheat varieties that exhibit both high quality and high yield, ensuring an overall increase in wheat production. Additionally, the consideration of phenotype parameters, such as grain length and width, holds significant importance in the introduction, screening, and evaluation of germplasm resources. Notably, a noteworthy positive association has been observed between grain size, grain shape, and grain weight. Simultaneously, within the scope of wheat breeding, the occurrence of inadequate harvest and storage practices can readily result in damage to wheat grains, consequently leading to a direct reduction in both emergence rate and yield. In essence, the integrity of wheat grains directly influences the wheat breeding process. Nevertheless, distinguishing between intact and damaged grains remains challenging due to the minimal disparities in certain characteristics, thereby impeding the accurate identification of damaged wheat grains through manual means. Consequently, this study aims to address this issue by focusing on the detection of wheat kernel integrity and completing the attainment of grain phenotype parameters. [Methods] This study presented an enhanced approach for addressing the challenges of low detection accuracy, unclear segmentation of wheat grain contour, and missing detection. The proposed strategy involves utilizing the Cascade Mask R-CNN model and replacing the backbone network with ResNeXt to mitigate gradient dispersion and minimize the model's parameter count. Furthermore, the inclusion of Mish as an activation function enhanced the efficiency and versatility of the detection model. Additionally, a multilayer convolutional structure was introduced in the detector to thoroughly investigate the latent features of wheat grains. The Soft-NMS algorithm was employed to identify the candidate frame and achieve accurate segmentation of the wheat kernel adhesion region. Additionally, the ImCascade R-CNN model was developed. Simultaneously, to address the issue of low accuracy in obtaining grain contour parameters due to disordered grain arrangement, a grain contour-based algorithm for parameter acquisition was devised. Wheat grain could be approximated as an oval shape, and the grain edge contour could be obtained according to the mask, the distance between the farthest points could be iteratively obtained as the grain length, and the grain width could be obtained according to the area. Ultimately, a method for wheat kernel phenotype identification was put forth. The ImCascade R-CNN model was utilized to analyze wheat kernel images, extracting essential features and determining the integrity of the kernels through classification and boundary box regression branches. The mask generation branch was employed to generate a mask map for individual wheat grains, enabling segmentation of the grain contours. Subsequently, the number of grains in the image was determined, and the length and width parameters of the entire wheat grain were computed. [Results and Discussions] In the experiment on wheat kernel phenotype recognition, a comparison and improvement were conducted on the identification results of the Cascade Mask R-CNN model and the ImCascade R-CNN model across various modules. Additionally, the efficacy of the model modification scheme was verified. The comparison of results between the Cascade Mask R-CNN model and the ImCascade R-CNN model served to validate the proposed model's ability to significantly decrease the missed detection rate. The effectiveness and advantages of the ImCascade R-CNN model were verified by comparing its loss value, P-R value, and mAP_50 value with those of the Cascade Mask R-CNN model. In the context of wheat grain identification and segmentation, the detection results of the ImCascade R-CNN model were compared to those of the Cascade Mask R-CNN and Deeplabv3+ models. The comparison confirmed that the ImCascade R-CNN model exhibited superior performance in identifying and locating wheat grains, accurately segmenting wheat grain contours, and achieving an average accuracy of 90.2% in detecting wheat grain integrity. These findings serve as a foundation for obtaining kernel contour parameters. The grain length and grain width exhibited average error rates of 2.15% and 3.74%, respectively, while the standard error of the aspect ratio was 0.15. The statistical analysis and fitting of the grain length and width, as obtained through the proposed wheat grain shape identification method, yielded determination coefficients of 0.9351 and 0.8217, respectively. These coefficients demonstrated a strong agreement with the manually measured values, indicating that the method is capable of meeting the demands of wheat seed testing and providing precise data support for wheat breeding. [Conclusions] The findings of this study can be utilized for the rapid and precise detection of wheat grain integrity and the acquisition of comprehensive grain contour data. In contrast to current wheat kernel recognition technology, this research capitalizes on enhanced grain contour segmentation to furnish data support for the acquisition of wheat kernel contour parameters. Additionally, the refined contour parameter acquisition algorithm effectively mitigates the impact of disordered wheat kernel arrangement, resulting in more accurate parameter data compared to existing kernel appearance detectors available in the market, providing data support for wheat breeding and accelerating the cultivation of high-quality and high-yield wheat varieties.

Key words: wheat breeding, ImCascade R-CNN model, grain integrity, semantic segmentation, grain phenotype parameters, deep learning