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GCNet: A Deep Learning Framework for Enhanced Grape Cluster Segmentation and Yield Estimation Incorporating Occluded

Rubi Quiñones1, Syeda Mariah Banu1, Eren Gultepe1

  • 1Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA.

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|February 25, 2025
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Summary

Grape Counting Network (GCNet) improves grape yield estimation by accurately counting occluded grapes using deep learning and correction factors. This novel framework enhances agricultural imaging analysis for better grape analytics.

Keywords:
convolutional neural networksdeep learningfeature extractiongrape object detectiongrape segmentationunsupervised learning

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Area of Science:

  • Agricultural Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Object segmentation algorithms for grape yield estimation are limited by their inability to count occluded grapes.
  • Hidden grapes due to cluster compactness or canopy interference lead to inaccurate yield predictions.

Purpose of the Study:

  • To develop a novel framework, Grape Counting Network (GCNet), for accurate grape cluster segmentation and yield estimation.
  • To address the challenge of counting occluded grapes in indoor agricultural imaging.

Main Methods:

  • Proposed the Grape Counting Network (GCNet), integrating deep learning with correction factors for occlusion adjustments.
  • Developed GrapeSet, a new dataset with indoor grape cluster imagery and ground truth data.
  • Utilized advanced object segmentation techniques for improved grape analytics.

Main Results:

  • GCNet achieved a R² of 0.96 and reduced mean absolute error (MAE) by 10% compared to previous methods.
  • Demonstrated enhanced segmentation accuracy under challenging conditions like foliage and cluster compactness.
  • Successfully addressed the limitation of counting only visible grapes.

Conclusions:

  • GCNet sets new standards in agricultural indoor imaging analysis for grape yield estimation.
  • The framework provides accurate object segmentation for grape analytics, aiding early yield prediction for harvesters.
  • Encourages future research into grape feature analysis for yield estimation.