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Related Experiment Video

Updated: Jun 23, 2026

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

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

Hongliang Ma¹, Maoxing Song¹, Mengying Yang¹

¹Tangshan Academy of Agricultural Sciences, Tangshan, China.

Frontiers in Plant Science

|June 22, 2026

Summary

A new lightweight object detection model, LiteMS-YOLO, accurately identifies wheat spikes for precision agriculture. This efficient model significantly reduces parameters while maintaining high detection accuracy, aiding in yield estimation.

Keywords:

YOLO-based model lightweight object detection multi-scale feature extraction small object detection wheat spike detection

Related Experiment Videos

Last Updated: Jun 23, 2026

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

Wheat spike detection is crucial for accurate yield estimation in precision agriculture.
Existing methods face challenges due to small target size, dense distribution, and complex field conditions.

Purpose of the Study:

To develop a lightweight and efficient object detection framework for wheat spike detection.
To improve spatial-semantic feature interaction and multi-scale feature representation for enhanced accuracy.

Main Methods:

Proposed LiteMS-YOLO, a lightweight object detection framework based on YOLO26n.
Integrated Feature Complementary Mapping (FCM) and Multi-Kernel Perception (MKP) modules.
Implemented targeted redundancy reduction strategies to decrease model complexity.

Main Results:

LiteMS-YOLO achieved mAP50 of 92.28% and mAP50-95 of 52.56% on a combined dataset.
The model uses only 0.627 million parameters, a significant reduction compared to YOLO26n and YOLOv8n.
Demonstrated competitive accuracy with substantially lower parameter count.

Conclusions:

LiteMS-YOLO offers an excellent balance between detection accuracy and computational efficiency.
The framework is well-suited for real-time deployment in resource-constrained agricultural environments.
Facilitates improved wheat yield estimation through advanced precision agriculture techniques.