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Related Concept Videos

Phloem and Sugar Transport02:02

Phloem and Sugar Transport

Like many living organisms, plants have tissues that specialize in specific plant functions. For example, shoots are well adapted to rapid growth, while roots are structured to acquire resources efficiently. However, sugar production is primarily restricted to the photosynthetic cells that reside in the leaves of angiosperm plants. Sugar and other resources are transported from photosynthetic tissues to other specialized tissues by a process called translocation.
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Fruit Development, Structure, and Function

Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.
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Sugar (a simple carbohydrate) metabolism (chemical reactions) is a classic example of the many cellular processes that use and produce energy. Living things consume sugar as a major energy source because sugar molecules have considerable energy stored within their bonds. Consumed carbohydrates have their origins in photosynthesizing organisms like plants. During photosynthesis, plants use the energy of sunlight to convert carbon dioxide gas into sugar molecules, like glucose. Because this...
Sugars as Energy Storage Molecules01:10

Sugars as Energy Storage Molecules

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Related Experiment Videos

YOLO-FSEP: An Improved YOLOv8n Algorithm for Sugar Orange Detection in Orchards.

Tianfa Deng1, Jinchao Sun1, Qingjuan Zhao1

  • 1Key Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

An improved YOLOv8n algorithm, YOLO-FSEP, enhances sugar orange detection in orchards by incorporating attention modules and advanced loss functions. This boosts accuracy and recall, enabling real-time robotic harvesting applications.

Keywords:
Focal_SIOU loss functionP6 detection headSCSA attention mechanismYOLOv8nsugar orange detection

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Agricultural Robotics
  • Machine Learning

Background:

  • Detecting sugar orange fruits in orchards is challenging due to occlusion from leaves, branches, and dense growth.
  • Existing methods often result in missed detections, false positives, and low confidence scores.

Purpose of the Study:

  • To develop an improved object detection algorithm for accurate sugar orange identification in complex orchard environments.
  • To enhance feature extraction, improve detection accuracy for difficult samples, and optimize multi-scale object performance.

Main Methods:

  • An improved algorithm based on YOLOv8n, named YOLO-FSEP, was proposed.
  • A Spatial-Channel Synergistic Attention (SCSA) module was integrated for enhanced feature extraction.
  • The IoU loss function was replaced with Focal_SIOU, and an SE attention mechanism with a P6 detection layer was added.

Main Results:

  • YOLO-FSEP achieved a 0.9% increase in accuracy, 1.3% in recall, and 3.2% in mAP50-95 compared to YOLOv8n.
  • The model maintained an inference speed of 62.6 FPS.
  • Real-time 3D localization pipeline achieved a total processing time of 50.15 ms, meeting robotic harvesting requirements.

Conclusions:

  • The YOLO-FSEP algorithm significantly improves sugar orange detection in challenging orchard conditions.
  • The model's real-time performance is suitable for dynamic localization in automated agricultural tasks like harvesting.
  • The proposed enhancements offer a robust solution for precision agriculture and fruit detection.