LiteMS-YOLO: a lightweight framework for small target detection in complex wheat field environments

Abstract

Wheat spike detection is essential for yield estimation in precision agriculture, yet it remains challenging due to the small size of targets, dense distribution, and complex field environments. In this study, we propose LiteMS-YOLO, a lightweight object detection framework based on YOLO26n. The model integrates a Feature Complementary Mapping (FCM) module to enhance spatial-semantic feature interaction and a Multi-Kernel Perception (MKP) unit to improve multi-scale feature representation. In addition, targeted redundancy reduction strategies are introduced to significantly lower model complexity. Experiments are conducted on a combined dataset comprising the public Global Wheat Head Detection (GWHD) dataset and 100 field images collected by the Tangshan Academy of Agricultural Sciences, with a total of 6,378 high-resolution images and over 44,000 annotated wheat spikes. LiteMS-YOLO achieves a mAP50 of 92.28% and a mAP50–95 of 52.56%, while using only 0.627 million parameters. Compared with YOLO26n and YOLOv8n, the proposed method reduces parameters by approximately 75% and 79%, respectively, while maintaining competitive accuracy. These results demonstrate that LiteMS-YOLO strikes an excellent balance between detection accuracy and efficiency, making it well-suited for real-time deployment in resource-constrained agricultural scenarios.

Plant phenotyping relevance

小麦穂の検出による収量推定を目的に、画像ベースの検出モデルを開発し、複数データセットで性能検証している。植物器官の検出・計数に基づく表現型取得が研究の中心である。

Code and data availability

The article describes wheat spike detection experiments on the public GWHD dataset plus 100 self-collected Tangshan field images, but contains no data availability statement, no author code/model deposit, and no public URL for the combined dataset, trained LiteMS-YOLO model, or analysis scripts. GWHD is a cited prior公共

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