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Phytopathogens represent an ongoing threat to crop production and a significant impediment to global food security. During the infection process, these pathogens spatiotemporally deploy a large array of effectors to sabotage host defense machinery and/or manipulate cellular pathways, thereby facilitating colonization and infection. However, besides their pivotal roles in pathogenesis, certain effectors, known as avirulence (AVR) effectors, can be directly or indirectly perceived by plant resistance (R) proteins, leading to race-specific resistance. An in-depth understanding of the intricate AVR-R interactions is instrumental for genetic improvement of crops and safeguarding them from diseases. Magnaporthe oryzae (M. oryzae), the causative agent of rice blast disease, is an exceptionally virulent and devastating fungal pathogen that induces blast disease on over 50 monocot plant species, including economically important crops. Rice-M. oryzae pathosystem serves as a prime model for functional dissection of AVR effectors and their interactions with R proteins and other target proteins in rice due to its scientific advantages and economic importance. Significant progress has been made in elucidating the potential roles of AVR effectors in the interaction between rice and M. oryzae over the past two decades. This review comprehensively discusses recent advancements in the field of M. oryzae AVR effectors, with a specific focus on their multifaceted roles through interactions with corresponding R/target proteins in rice during infection. Furthermore, we deliberated on the emerging strategies for engineering R proteins by leveraging the structural insights gained from M. oryzae AVR effectors.

The rice panicle traits substantially influence grain yield, making them a primary target for rice phenotyping studies. However, most existing techniques are limited to controlled indoor environments and have difficulty in capturing the rice panicle traits under natural growth conditions. Here, we developed PanicleNeRF, a novel method that enables high-precision and low-cost reconstruction of rice panicle three-dimensional (3D) models in the field based on the video acquired by the smartphone. The proposed method combined the large model Segment Anything Model (SAM) and the small model You Only Look Once version 8 (YOLOv8) to achieve high-precision segmentation of rice panicle images. The neural radiance fields (NeRF) technique was then employed for 3D reconstruction using the images with 2D segmentation. Finally, the resulting point clouds are processed to successfully extract panicle traits. The results show that PanicleNeRF effectively addressed the 2D image segmentation task, achieving a mean F1 score of 86.9% and a mean Intersection over Union (IoU) of 79.8%, with nearly double the boundary overlap (BO) performance compared to YOLOv8. As for point cloud quality, PanicleNeRF significantly outperformed traditional SfM-MVS (structure-from-motion and multi-view stereo) methods, such as COLMAP and Metashape. The panicle length was then accurately extracted with the rRMSE of 2.94% for indica and 1.75% for japonica rice. The panicle volume estimated from 3D point clouds strongly correlated with the grain number (R2 = 0.85 for indica and 0.82 for japonica) and grain mass (0.80 for indica and 0.76 for japonica). This method provides a low-cost solution for high-throughput in-field phenotyping of rice panicles, accelerating the efficiency of rice breeding.