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RTD-YOLO: a precise quality detection and grading model for rose tea.

Zezhong Ding¹, Zhiwei Chen², Bin Hu^3,4,5

¹College of Mechanical and Electronic Engineering, Shihezi University, Shihezi, China.

NPJ Science of Food

|June 5, 2026

Summary

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Classification of Titrimetric Analysis Based on Reaction Types

Classification of Titrimetric Analysis Based on Reaction Types

Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...

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This summary is machine-generated.

This study introduces an improved YOLOv12n model for automated rose tea quality grading, enhancing market competitiveness. The new model achieves higher accuracy and efficiency, enabling real-time deployment on edge devices.

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

The commercial value of roses necessitates quality control in rose tea production.
Current manual grading of rose tea is inefficient and lacks objectivity.
Deploying large-scale models for real-time quality assessment presents challenges.

Purpose of the Study:

To develop an efficient and accurate automated system for rose tea quality detection and grading.
To improve the market competitiveness of rose tea through intelligent classification.
To address the limitations of manual grading and large model deployment.

Main Methods:

An enhanced YOLOv12n model was proposed for rose tea grading.
Key modifications included integrating the SEFF module, replacing Conv with ADown, and constructing a parameter-sharing detection head.

Related Experiment Videos

The model was optimized using the Lamp pruning method.

Main Results:

The improved model achieved 90.48% F1-score and 95.85% mAP.
It demonstrated a significant increase in FPS (298.5) and real-time deployment capability (55.3 FPS on Jetson Orin NX).
The model size was reduced to 2.0MB with decreased FLOPs and Params.

Conclusions:

The developed model offers a viable solution for intelligent and automated rose tea grading.
It provides technical support for enhancing the quality and marketability of rose tea.
The model's efficiency and accuracy make it suitable for edge device deployment in production environments.