# IV-YOLO: an information vortex-based progressive fusion method for accurate rice detection

## TL;DR

This paper introduces IV-YOLO, a new model for detecting rice plants in drone images, improving accuracy in precision agriculture.

## Contribution

The novel IV-YOLO model uses a Multi-scale Spiral Information Vortex and Gradual Feature Fusion Neck for better rice detection.

## Key findings

- IV-YOLO achieves a precision of 0.8581 on the DRPD dataset.
- It outperforms YOLOv5–YOLOv11 and FRPNet across all metrics.
- The model effectively disentangles rice plant features from background clutter.

## Abstract

In the context of precision agriculture, the problems of adhesion of rice plant features and background interference in UAV remote sensing images make traditional models difficult to meet the requirements of individual plant-level detection. To address this, this paper proposes an Information Vortex-based progressive fusion YOLO (IV-YOLO) model. Firstly, a Multi-scale Spiral Information Vortex (MSIV) module is designed, which achieves the disentanglement of adhered rice plant features and decoupling of background clutter through multi-scale rotational kernel convolution and channel-spatial joint reconstruction. Secondly, a Gradual Feature Fusion Neck (GFEN) is constructed to synergize the high-resolution details of shallow features (such as tiller edges and panicle textures) with the high semantic information of deep features, generating multi-scale feature representations with both discriminativeness and completeness. Experiments conducted on the public DRPD dataset show that IV-YOLO achieves a Precision of 0.8581, outperforming YOLOv5–YOLOv11 and FRPNet across all metrics. This study provides a reliable technical solution for individual plant-level rice monitoring and facilitates the large-scale implementation of precision agriculture.