Rice-SVBDete: a detection algorithm for small vascular bundles in rice stem's cross-sections.

Abstract

Introduction Vascular bundles play a vital role in the growth, development, and yield formation of rice. Accurate measurement of their structure and distribution is essential for improving rice breeding and cultivation strategies. However, the detection of small vascular bundles from cross-sectional images is challenging due to their tiny size and the noisy background typically present in microscopy images. Methods To address these challenges, we propose Rice-SVBDete, a specialized deep learning-based detection algorithm for small vascular bundles in rice stem cross-sections. Our approach enhances the YOLOv8 architecture by incorporating three key innovations: Dynamic Snake-shaped Convolution (DSConv) in the Backbone network to adaptively capture intricate structural details of small targets. A Multi-scale Feature Fusion (MFF) mechanism, combining features from the Backbone, Feature Pyramid Network (FPN), and Path Aggregation Network (PAN), to better handle objects at multiple scales. A new Powerful Intersection over Union (PIoU) loss function that emphasizes spatial consistency and positional accuracy, replacing the standard CIoU loss. Results Experimental evaluations show that Rice-SVBDete achieves a precision of 0.789, recall of 0.771, and mean Average Precision (mAP@.5) of 0.728 at an IoU threshold of 0.50. Compared to the baseline YOLOv8, Rice-SVBDete improves precision by 0.179, recall by 0.201, and mAP@.5 by 0.227, demonstrating its effectiveness in small object detection. Discussion These results highlight Rice-SVBDete's potential for accurately identifying small vascular bundles in complex backgrounds, providing a valuable tool for rice anatomical analysis and supporting advancements in precision agriculture and plant science research.

Code and data availability

The supplied blocks describe a custom rice stem cross-section dataset (1091 microscopic images, 66728 annotations) 'provided by Guangxi University' with no public deposit, URL, or availability language, and no authors' code release. The only URL mentioned (github.com/ultralytics/ultralytics) is the generic YOLOv8 third

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