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Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm.

Wei Han1, Fei Jiang2, Zhiyuan Zhu1

  • 1Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.

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A novel image processing technique using the flooding filling algorithm significantly improved cherry quality detection accuracy to 99.6%. This AI-driven approach enhances fruit quality assessment, overcoming limitations of manual inspection and traditional chemical methods.

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Current cherry quality assessment relies on subjective human senses, leading to inaccuracies and inefficiency.
  • Chemical methods for internal quality analysis are time-consuming and reduce detection speed.
  • Artificial intelligence (AI) and image processing offer potential for automated, accurate fruit quality evaluation.

Purpose of the Study:

  • To enhance the accuracy and efficiency of cherry quality detection using AI-powered image processing.
  • To address the limitations of existing methods in accurately assessing fruit quality.
  • To investigate the impact of image preprocessing on AI model performance for cherry recognition.

Main Methods:

  • Utilized the You Only Look Once version 5 (YOLOv5) model for cherry image recognition.
  • Applied the flooding filling algorithm to extract cherry images, creating a refined dataset.
  • Trained and compared the YOLOv5 model using both extracted and non-extracted cherry image datasets.

Main Results:

  • The YOLOv5 model trained on the flooding filling algorithm-extracted dataset achieved an accuracy rate of 99.6% after only 20 training epochs.
  • In contrast, the model trained on non-extracted images reached only 78.6% accuracy after 300 training epochs.
  • Image preprocessing with the flooding filling algorithm substantially improved detection accuracy and reduced training time.

Conclusions:

  • The flooding filling algorithm is an effective preprocessing step for improving AI-based cherry quality detection.
  • AI image processing, particularly with optimized datasets, offers a superior alternative to traditional methods for fruit quality assessment.
  • This study demonstrates a significant advancement in automated agricultural product quality control.