HDMS-YOLO: a multi-scale weed detection model for complex farmland environments

TL;DR

HDMS-YOLO is a new model for detecting weeds in farmland, improving accuracy and performance over existing methods.

Contribution

The paper introduces HDMS-YOLO with novel modules for better weed feature extraction and detection.

Findings

HDMS-YOLO achieves 74.2% accuracy, 66.3% recall, and 71.2% mAP in weed detection.

The model outperforms YOLO11 by 2.6%, 2.1%, and 2.6% in accuracy, recall, and mAP respectively.

HDMS-YOLO shows superior performance compared to other mainstream algorithms in complex farmland environments.

Abstract

With the continuous advancement of agricultural technology, automatic weed removal has become increasingly important for precision agriculture. However, accurate weed identification remains challenging due to the diversity and varying sizes of weeds, as well as the high visual similarity between weeds and crops in terms of shape, colour, and texture.

To address these challenges, this study proposes the HDMS-YOLO model for robust weed identification, trained and evaluated on the publicly available CropAndWeed dataset. The model incorporates two novel feature extraction modules—the Shallow and Deep Receptive Field Distillation (SRFD and DRFD) modules—to effectively capture both shallow and deep weed features. The traditional C3K2 structure is replaced by the Partial Convolution-based Multi-Scale Feature Aggregation (PC-MSFA) module, which enhances feature representation through partial convolution and residual connections. In addition, a new IntegraDet dynamic task-alignment detection head is designed to further improve localisation and classification accuracy.

Experimental results show that HDMS-YOLO achieves an accuracy of 74.2%, a recall of 66.3%, and an mAP of 71.2%, which are 2.6%, 2.1%, and 2.6% higher, respectively, than those of YOLO11. Compared with other mainstream algorithms, HDMS-YOLO demonstrates superior overall detection performance.

The proposed HDMS-YOLO model exhibits strong capability in extracting and representing weed features, leading to improved identification accuracy and generalisation. These results highlight its potential application in precision farm management and the development of intelligent weed-removal robots for unmanned agricultural systems.

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