Work

YOLOv11-DAFNet: A multi-directional attention and multi-dilated residual fusion network for cucumber downy mildew sporangia detection in microscopic images

Year: 2025

Type: article

Abstract: To address the challenges in microscopic image detection of cucumber downy mildew sporangia, where extremely small target sizes and pronounced geometric deformations hinder detection performance, this study proposes a novel detection model, YOLOv11-DAFNet. First, to overcome the limitations of conventional convolutions in accurately representing edge pixels and capturing multi-directional features of sporangia due to fixed convolutional orientations, a Directional Attention Fusion Convolution (DAFConv) is designed, which effectively enhances the modeling capability for multi-directional edge features. Second, in response to the limited receptive field of the original C3k2 module and its insufficient adaptability to the multi-scale deformation of sporangia, a Multi-Dilated Residual Block (C3k2_MDRBlock) is introduced. By embedding multiple dilation rate convolution kernels into the C3k2 structure, this module strengthens the network’s ability to perceive complex shapes and multi-scale features. Finally, to reinforce the interaction and fusion between shallow and deep features, a Context-Aware Attention Fusion Module (CAFMFusion) is proposed, which integrates a context-aware mechanism with pixel attention to adaptively generate fine-grained pixel-level weights, thereby enabling precise, dynamic spatial fusion and selection between shallow and deep features. Experimental results demonstrate that YOLOv11-DAFNet achieves an [email protected] of 88.2% on the test dataset, representing a 6.6 percentage point improvement over the baseline YOLOv11n, while precision and recall are increased to 90.6% and 85.1%, respectively. This work provides a fast and accurate detection approach for cucumber downy mildew sporangia.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101535

Authors: Zhijun Zhang, Kaige Liu, Zhihua Zha, Ming Li

Institutions: Shihezi University, China Meteorological Administration, Beijing Academy of Agriculture and Forestry Sciences, Ministry of Agriculture and Rural Affairs, National Engineering Research Center for Information Technology in Agriculture

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,770