[Objectives]This study was conducted to find out regulatory genes related to purple in spears of asparagus(Asparagus officinalis L.).[Methods]The stable asparagus inbred line JX1513-5(the base of the spear is purple)and JLV1718-7(the base of the spear is green)were used as parents to study the genetic law of purple/green traits in their offspring.[Results]The results showed that the purple in the basal part of asparagus spear was controlled by a pair of alleles,and purple was dominant over green.The F 2 segregation population was resequenced by the bulk segregation analysis(BSA)method,and the purple trait in the basal part of asparagus spear was located in the interval of 24.51-25.08 Mb on Chr07 chromosome,which included 47 genes.According to the annotation information,three candidate genes were screened out:LOC109849403,LOC109849430 and LOC109849442.The candidate genes were verified by real-time fluorescence quantitative PCR(qRT-PCR),and finally LOC109849442 was obtained as the candidate gene for controlling the purple/green trait in the basal part of asparagus spear.[Conclusions]This study lays a foundation for the breeding of new asparagus varieties and molecular marker-assisted breeding.
Tao LIUWenlai RENYuqin LIANGYubo WANGWei LIUXing WANGYanpo CAO
Asparagus stem blight,also known as“asparagus cancer”,is a serious plant disease with a regional distribution.The widespread occurrence of the disease has had a negative impact on the yield and quality of asparagus and has become one of the main problems threatening asparagus production.To improve the ability to accurately identify and localize phenotypic lesions of stem blight in asparagus and to enhance the accuracy of the test,a YOLOv8-CBAM detection algorithm for asparagus stem blight based on YOLOv8 was proposed.The algorithm aims to achieve rapid detection of phenotypic images of asparagus stem blight and to provide effective assistance in the control of asparagus stem blight.To enhance the model’s capacity to capture subtle lesion features,the Convolutional Block AttentionModule(CBAM)is added after C2f in the head.Simultaneously,the original CIoU loss function in YOLOv8 was replaced with the Focal-EIoU loss function,ensuring that the updated loss function emphasizes higher-quality bounding boxes.The YOLOv8-CBAM algorithm can effectively detect asparagus stem blight phenotypic images with a mean average precision(mAP)of 95.51%,which is 0.22%,14.99%,1.77%,and 5.71%higher than the YOLOv5,YOLOv7,YOLOv8,and Mask R-CNN models,respectively.This greatly enhances the efficiency of asparagus growers in identifying asparagus stem blight,aids in improving the prevention and control of asparagus stem blight,and is crucial for the application of computer vision in agriculture.