Root-knot nematodes can infect over 2000 plants, which causes significant economic losses. Rapid and accurate detection of root-knot nematode disease is the key to screening resistant varieties and evaluating the effect of prevention and control. To address the challenge of detecting root-knot nematode disease caused by dense root knots, multiple fibrous roots, and small root knots, this paper takes cucumber as the research object to propose a detection and grading model Root-YOLOv7. Specifically, the backbone network of YOLOv7 is restructured, which enables the proposed model to better capture object information at different scales through a hierarchical structure and a self-attention module based on shifted windows. Additionally, combined with the Wise-IoU loss function, the proposed model can adaptively adjust the weight of the overlapping part of the object box, which solves the problem that bounding box regression cannot be optimized effectively when detecting low-quality objects. Furthermore, an improved head network structure is proposed to compute the attention weights by capturing the cross-dimension interaction of the root knot feature between the spatial and channel dimensions. To verify the effectiveness of the proposed model, the performance of Root-YOLOv7 is compared with typical object detection models. Experimental results show that the AP@0.5 of Root-YOLOv7 reaches 87.40%, which is 72.67%, 5.60%, 5.28%, 9.68%, 5.83%, and 7.45% higher than Faster R-CNN, RT-DETR, YOLOv5, YOLOv6, YOLOv7, and YOLOv8, respectively. The proposed approach is expected to reduce the workload of plant pathologists and provide technical support for the cultivation of plant varieties with disease resistance.

Soybean cyst nematode (SCN, Heterodera glycines) is a devastating pathogen that infects soybean (Glycine max L. Merrill) and disrupts soybean production worldwide. SCN infection upregulates or downregulates the expression of multiple genes in soybean. However, the regulatory mechanisms that underlie these changes in gene expression remain largely unexplored. N⁶-methyladenosine (m⁶A) methylation, one of the most prevalent mRNA modifications, contributes to transcriptional reprogramming during plant responses to pathogen infection. Nevertheless, the role of m⁶A methylation in establishing compatible and incompatible soybean responses to SCN has not previously been studied. Here, we performed transcriptome-wide m⁶A profiling of soybean roots infected with virulent and avirulent populations of SCN. Compared with the compatible response, the incompatible response was associated with higher global m⁶A methylation levels, as well as more differentially modified m⁶A peaks (DMPs) and differentially expressed genes (DEGs). A total of 133 and 194 genes showed significant differences in both transcriptional expression and m⁶A methylation levels in compatible and incompatible interactions; the most significantly enriched gene ontology terms associated with these genes were plant–pathogen interaction (compatible) and folate biosynthesis (incompatible). Our findings demonstrate that the m⁶A methylation profiles of compatible and incompatible soybean responses are distinct and provide new insights into the regulatory mechanism underlying soybean response to SCN at the post-transcriptional modification level, which will be valuable for improving the SCN-resistant breeding.