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Blackberry Fruit Classification in Underexposed Images Combining Deep Learning and Image Fusion Methods.

Eduardo Morales-Vargas1, Rita Q Fuentes-Aguilar1, Emanuel de-la-Cruz-Espinosa2

  • 1Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Av. Gral Ramón Corona No 2514, Colonia Nuevo México, Zapopan 45201, Jalisco, Mexico.

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Summary
This summary is machine-generated.

This study introduces an image fusion method to improve blackberry ripeness classification in varying light conditions. The technique enhances low-light images, boosting classification accuracy for automated harvesting systems.

Keywords:
blackberry classificationclassification methodsfeature fusionripeness stage classification

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Area of Science:

  • Computer Vision
  • Agricultural Technology

Background:

  • Increasing berry production faces challenges with labor shortages and fruit waste.
  • Uncontrolled lighting in agricultural settings causes image underexposure, hindering accurate ripeness classification.
  • Distinguishing blackberry ripeness is difficult due to their dark color and variable lighting.

Purpose of the Study:

  • To automate blackberry ripeness classification under diverse lighting conditions.
  • To enhance image quality using fusion methods for improved computer vision analysis.
  • To address challenges posed by underexposed images in agricultural computer vision tasks.

Main Methods:

  • Developed an algorithm combining visible, enhanced visible, and near-infrared spectral images.
  • Employed image fusion techniques to improve input image quality before classification.
  • Evaluated performance on underexposed and outdoor images, analyzing fusion metrics.

Main Results:

  • Achieved a mean F1 score of 0.909±0.074 without fine-tuning and 0.962±0.028 with fine-tuning on underexposed images.
  • Demonstrated up to a 12% increase in classification rates in some cases.
  • Confirmed the method's utility in enhancing outdoor images, improving contrast without altering color saturation.

Conclusions:

  • Image fusion effectively improves blackberry ripeness classification in low-light conditions.
  • The proposed method enhances image quality for computer vision applications in agriculture.
  • Weighted fusion offers a viable solution for improving contrast in underexposed vegetation images.