Identification and characterization of a new soybean promoter induced by Phakopsora pachyrhizi, the causal agent of Asian soybean rust

A deep semantic network-based image segmentation of soybean rust pathogens

Introduction

Asian soybean rust is a highly aggressive leaf-based disease triggered by the obligate biotrophic fungus Phakopsora pachyrhizi which can cause up to 80% yield loss in soybean. The precise image segmentation of fungus can characterize fungal phenotype transitions during growth and help to discover new medicines and agricultural biocides using large-scale phenotypic screens.

Methods

The improved Mask R-CNN method is proposed to accomplish the segmentation of densely distributed, overlapping and intersecting microimages. First, Res2net is utilized to layer the residual connections in a single residual block to replace the backbone of the original Mask R-CNN, which is then combined with FPG to enhance the feature extraction capability of the network model. Secondly, the loss function is optimized and the CIoU loss function is adopted as the loss function for boundary box regression prediction, which accelerates the convergence speed of the model and meets the accurate classification of high-density spore images.

Results

The experimental results show that the mAP for detection and segmentation, accuracy of the improved algorithm is improved by 6.4%, 12.3% and 2.2% respectively over the original Mask R-CNN algorithm.

Discussion

This method is more suitable for the segmentation of fungi images and provide an effective tool for large-scale phenotypic screens of plant fungal pathogens.

Soybean-Phakopsora pachyrhizi interactions: towards the development of next-generation disease-resistant plants

Soybean rust (SBR), caused by the obligate biotrophic fungus Phakopsora pachyrhizi, is a devastating foliar disease threatening soybean production. To date, no commercial cultivars conferring durable resistance to SBR are available. The development of long-lasting SBR resistance has been hindered by the lack of understanding of this complex pathosystem, encompassing challenges posed by intricate genetic structures in both the host and pathogen, leading to a gap in the knowledge of gene-for-gene interactions between soybean and P. pachyrhizi. In this review, we focus on recent advancements and emerging technologies that can be used to improve our understanding of the P. pachyrhizi-soybean molecular interactions. We further explore approaches used to combat SBR, including conventional breeding, transgenic approaches and RNA interference, and how advances in our understanding of plant immune networks, the availability of new molecular tools, and the recent sequencing of the P. pachyrhizi genome could be used to aid in the development of better genetic resistance against SBR. Lastly, we discuss the research gaps of this pathosystem and how new technologies can be used to shed light on these questions and to develop durable next-generation SBR-resistant soybean plants.