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Related Concept Videos

Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Improved Real-Time Detection Transformer with Low-Frequency Feature Integrator and Token Statistics Self-Attention for Automated Grading of <i>Stropharia rugoso-annulata</i> Mushroom.

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Updated: May 12, 2026

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry
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APHS-YOLO: A Lightweight Model for Real-Time Detection and Classification of Stropharia Rugoso-Annulata.

Ren-Ming Liu1, Wen-Hao Su1

  • 1College of Engineering, China Agricultural University, Haidian, Beijing 100083, China.

Foods (Basel, Switzerland)
|June 19, 2024
PubMed
Summary

Automated sorting of Stropharia rugoso-annulata mushrooms is improved with the lightweight APHS-YOLO model. This efficient system enhances accuracy and speed on resource-limited devices, aiding forest farmers.

Keywords:
Stropharia rugoso-annulataautomatic sortinghigh-level screen feature pyramidknowledge distilllightweight model

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Area of Science:

  • Agricultural technology
  • Computer vision
  • Machine learning

Background:

  • Manual sorting of Stropharia rugoso-annulata mushrooms is prone to bias and inefficiency.
  • Real-time automated sorting of mushrooms presents challenges in identification, localization, and high-volume processing.
  • Deploying accurate and fast models on resource-limited devices is a significant hurdle.

Purpose of the Study:

  • To develop a lightweight and efficient model for automated grading and seasonal classification of Stropharia rugoso-annulata.
  • To address the limitations of current manual sorting methods and real-time automated sorting challenges.

Main Methods:

  • Proposed the APHS-YOLO model, integrating YOLOv8n with AKConv, CSPPC, and HSFPN modules.
  • Utilized a comprehensive dataset of Stropharia rugoso-annulata runners from different grades and seasons (spring and autumn).
  • Employed High-Level Screening Feature Pyramid Networks (HSFPNs) for feature extraction and a knowledge refinement technique to maintain accuracy.

Main Results:

  • The APHS-YOLO model demonstrated significant reductions in memory usage (57.8%) and computational resources (62.5%) compared to the original model.
  • Achieved a high processing speed with frames per second (FPS) exceeding 100.
  • Obtained a 0.1% improvement in accuracy metrics while maintaining efficiency.

Conclusions:

  • The APHS-YOLO model offers an efficient and accurate solution for automated Stropharia rugoso-annulata mushroom sorting.
  • This research provides a valuable reference for developing practical automatic sorting equipment for the agricultural sector.
  • The lightweight design enables deployment on resource-constrained devices, facilitating widespread adoption by forest farmers.