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Fruit Development, Structure, and Function01:58

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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Automating Citrus Budwood Processing for Downstream Pathogen Detection Through Instrument Engineering
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RT-DETR-DCEA: A Lightweight Citrus Defective Fruit Detection Algorithm for Complex Orchard Environments.

Jihui Qiao1,2, Yuchen Sun2, Binyuan Zhong2,3

  • 1College of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming 650201, China.

Plants (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces RT-DETR-DCEA, a lightweight model for detecting defective citrus fruits in orchards, achieving high accuracy and recall with efficient processing. The model excels in identifying various defects despite challenging environmental conditions.

Keywords:
RT-DETRadaptive sparse attentiondefective citrus fruitdynamic convolutionlightweight detectionmulti-scale feature fusion

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Area of Science:

  • Computer Vision
  • Agricultural Technology
  • Machine Learning

Background:

  • Natural orchard environments present significant challenges for automated citrus fruit defect detection, including variations in fruit appearance, occlusion, and illumination.
  • Existing detection models often struggle with the complex visual characteristics of defects like lesions, mildew, and external damage.

Purpose of the Study:

  • To develop a lightweight and efficient deep learning model, RT-DETR-DCEA, for accurate detection of defective citrus fruits in complex orchard settings.
  • To enhance the model's ability to extract fine-grained features, fuse multi-scale information, recover details, and suppress background noise.

Main Methods:

  • Introduced a Dynamic Hybrid Convolution Module (DIMB) for fine-grained defective feature extraction, adapting to irregular defect shapes.
  • Designed a Content-Guided Attention Feature Fusion Network (CGAFN) for effective fusion of multi-scale features.
  • Implemented a lightweight upsampling enhancement module (EUCB-SC) and adaptive sparse self-attention (AIFI-ASSA) for detail recovery and noise suppression.

Main Results:

  • RT-DETR-DCEA achieved 92.1% Precision, 86.1% Recall, and 91.8% mAP@50 on a dataset of healthy, diseased, moldy, and externally damaged citrus fruits.
  • The model demonstrates a favorable balance between detection accuracy, recall, and lightweightness, with 1.477 × 10^7 parameters and 81 FPS inference speed.
  • Outperformed the original RT-DETR-R18 and various YOLO series models in terms of detection performance and efficiency.

Conclusions:

  • The proposed RT-DETR-DCEA model effectively addresses the challenges of citrus fruit defect detection in natural environments.
  • The model's architecture offers a promising solution for real-world agricultural applications requiring high accuracy and computational efficiency.
  • Further validation on larger, diverse datasets and edge devices is recommended for broader deployment.