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A Hybrid Compact Convolutional Transformer with Bilateral Filtering for Coffee Berry Disease Classification.

Biniyam Mulugeta Abuhayi1, Andras Hajdu2

  • 1Department of Information Technology, University of Gondar, Gondar P.O. Box 196, Ethiopia.

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|July 12, 2025
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Summary
This summary is machine-generated.

A new Compact Convolutional Transformer (CCT) model accurately detects coffee berry disease (CBD) in arabica coffee. This deep learning approach offers a sensitive and efficient solution for sustainable coffee production.

Keywords:
bilateral filteringcoffee berry diseasecompact convolution transformers

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Area of Science:

  • Agricultural Science
  • Plant Pathology
  • Computer Vision
  • Deep Learning

Background:

  • Coffee berry disease (CBD), caused by Colletotrichum kahawae, poses a significant threat to global Coffee arabica production, resulting in substantial yield losses.
  • Current methods for CBD detection are often subjective and inefficient, especially in resource-limited agricultural settings.
  • Existing deep learning research primarily focuses on leaf diseases, with limited attention to berry-specific infections like CBD.

Purpose of the Study:

  • To develop a lightweight and accurate deep learning model for the classification of healthy and CBD-affected coffee berries.
  • To address the gap in research concerning berry-specific plant disease detection using advanced AI techniques.
  • To provide a potential solution for real-time, low-resource deployment in sustainable coffee farming.

Main Methods:

  • A dataset of 1737 coffee berry images was preprocessed using bilateral filtering and color segmentation.
  • A Compact Convolutional Transformer (CCT) model, combining convolutional branches and a transformer encoder, was employed for feature extraction and classification.
  • The CCT model was integrated with a Multilayer Perceptron (MLP) classifier and optimized using early stopping and regularization techniques.

Main Results:

  • The proposed CCT model achieved a validation accuracy of 97.70% and 100% sensitivity for detecting CBD.
  • CCT-extracted features demonstrated strong performance with traditional classifiers like SVM (82.47% accuracy, AUC 0.91) and Decision Tree (82.76% accuracy, AUC 0.86).
  • The CCT system exhibited superior accuracy (97.5%) with significantly fewer parameters (0.408 million) and faster training times (2.3 s/epoch) compared to pretrained models.

Conclusions:

  • The developed lightweight CCT model offers a highly accurate and sensitive solution for identifying coffee berry disease.
  • The model's efficiency and low resource requirements make it suitable for real-time applications in sustainable coffee production.
  • This study highlights the potential of advanced deep learning architectures for addressing critical challenges in agricultural plant pathology.