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Multi head attention based deep learning framework for waxberry fruit object segmentation from high resolution remote

Rohan Vaghela1, N Sravya2, Shyam Lal2

  • 1Department of Computer Science & Engineering, Chandubhai S. Patel Institute of Technology, Charotar University of Science & Technology, Anand, 388421, Gujarat, India.

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|October 30, 2025
PubMed
Summary

This study introduces the Multi-Attention Waxberry Network (MAWNet) for automated fruit harvesting. MAWNet improves waxberry segmentation in challenging orchard conditions, achieving high accuracy.

Keywords:
AttentionConvolutional neural network (CNN)Deep learningSegmentationWaxberry

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Waxberry harvesting is labor-intensive, driving demand for automation.
  • Accurate fruit segmentation is crucial for automated harvesting but challenging in complex orchard environments.
  • Existing methods struggle with occlusions, overlapping fruits, and variable lighting.

Purpose of the Study:

  • To develop an advanced deep learning model for robust waxberry segmentation.
  • To address limitations of current methods in real-world harvesting scenarios.

Main Methods:

  • A novel fully convolutional neural network, MAWNet, was developed.
  • MAWNet features a UNet-based architecture incorporating enhanced residual blocks, transformer blocks, Atrous Spatial Pyramid Pooling (ASPP), and a Multiple Dilation Convolutional (MDC) block.
  • The model is designed to handle occlusions, fruit overlap, and lighting variations.

Main Results:

  • MAWNet achieved high performance metrics: 99.63% accuracy, 96.77% Intersection over Union (IoU), and 98.34% Dice coefficient.
  • Experimental results demonstrated MAWNet's superiority over several State-of-the-Art (SOTA) architectures.

Conclusions:

  • The proposed MAWNet effectively segments waxberries in complex orchard conditions.
  • MAWNet offers a significant advancement for automated fruit-picking systems, enhancing efficiency and accuracy.