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Related Experiment Video

Updated: Jan 15, 2026

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A SCG-YOLOv8n potato counting framework with efficient mobile deployment.

Leichao Du1, Jichao Li2, Jing Yang2

  • 1Gansu Academy of Agri-Engineering Technology, Lanzhou, 730030, Gansu, China.

Scientific Reports
|October 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces SCG-YOLOv8n, an efficient AI model for detecting and counting potatoes in challenging field conditions. It enables real-time mobile application for precision agriculture.

Keywords:
Lightweight modelMobile deploymentPotato detectionReal-time countingSmart agricultureYOLOv8n

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Accurate potato detection is crucial for yield estimation and automated sorting in agriculture.
  • Field conditions like soil occlusion and poor lighting pose challenges for current visual recognition systems.

Purpose of the Study:

  • To develop a compact, field-adapted object detection framework for early potato harvest monitoring.
  • To improve the accuracy and efficiency of potato counting and detection in real-world farming environments.

Main Methods:

  • Introduced SCG-YOLOv8n, a YOLOv8n-based framework with C-SPD module, S-CARAFE operator, and GhostShuffleConv layers.
  • Incorporated Float16 quantization for model compression to 3.2 MB, enabling real-time mobile inference.
  • Developed the PotatoDetector mobile application for field deployment and testing.

Main Results:

  • SCG-YOLOv8n demonstrated superior performance over YOLOv5n and its base version in field experiments.
  • The model achieved high accuracy in potato counting tasks, with an RMSE of 1.38 and R² of 0.96.
  • The compressed model enables real-time inference on Android devices via the PotatoDetector app.

Conclusions:

  • SCG-YOLOv8n provides a practical and scalable solution for precision agriculture, specifically for potato monitoring.
  • The framework shows potential for adaptation to other root and tuber crop detection scenarios.
  • The developed mobile application facilitates data-driven agricultural decision-making through accessible technology.