To solve the problems such as time-consuming and labor-intensive of manual measurement for soybean pod phenotypic parameters in traditional soybean seed evaluation processes,as well as the large quantity de-mand for manual data annotation,weak environmental ad-aptation and high computational costs in existing automated measurement methods,an automated measurement method for pod key phenotypic parameters which was mainly fo-cused on pod length and width measuring was proposed in this study,based on virtual dataset generation and rotated object detection analysis.An improved pod detection model(CSL-YOLOv7-tiny)was proposed by the method based on YOLOv7-tiny.The Circular Smooth Label was introduced to en-able the model to obtain the capability for rotated object detection,and to improve the quality of detecting elongated pod tar-gets in a disorganized arrangement.To avoid manual annotation of training data,virtual image generation method was used to get virtual pod dataset as well as virtual coin and pod mixture dataset containing annotation information.Transfer learning strategy was employed to transfer the model from the virtual pod dataset to the virtual coin and pod mixture dataset,which accumulated the model's ability in pod features extracting.A post-processing method based on K-means clustering was de-signed to analyze the detected rotated bounding boxes,and obtained pod length and width,which reduced measurement er-rors caused by shooting environmental differences.Experimental results showed that under the condition of no training data annotation,CSL-YOLOv7-tiny trained by virtual images obtained the optimal mAP0.50 and mAP0.50∶0.95 for coin and pod tar-gets detection,which were 99.3%and 78.0%,respectively.The model size and inference time were only 12.92 MB and 12.5 ms respectively,and the determination coefficients(R2)for pod length and width measurement reached 0.94 and 0.86 respectively,with only 0.42 mm and 0.02 mm differences compared with actual measurements.Furthermore,by compara-tive analysis of the proposed method,the advantages in model training,lightweight deployment and adaptation to different breeding environments were validated.The research results can provide reference for development of automated and intelli-gent measurement system of soybean pod phenotypic parameter and can support the acceleration of high-quality and high-yield soybean breeding.
关键词
大豆考种/豆荚表型/虚拟数据/旋转目标检测/YOLOv7-tiny
Key words
soybean seed evaluation/soybean pod phenotype/virtual data/rotated object detection/YOLOv7-tiny
维普
万方数据