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DBA-ViNet: an effective deep learning framework for fruit disease detection and classification using explainable AI.

Saravanan Srinivasan1, Lalitha Somasundharam2, Sukumar Rajendran3

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, 600089, India.

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|July 29, 2025
PubMed
Summary
This summary is machine-generated.

A new Dual-Branch Attention-Guided Vision Network (DBA-ViNet) model accurately identifies fruit diseases in apples, guavas, mangoes, pomegranates, and oranges. This computer vision approach offers high accuracy for smart agriculture and crop health monitoring.

Keywords:
Computer visionConvNet modelsDBA-ViNetDeep learningFruit disease classification

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Area of Science:

  • Computer Vision
  • Agricultural Technology
  • Plant Pathology

Background:

  • Accurate disease identification in fruits is crucial for agricultural productivity and food security.
  • Existing computer vision models face challenges in effectively integrating global and local features for precise disease detection.
  • The need for robust automated systems in smart agriculture necessitates advanced image analysis techniques.

Purpose of the Study:

  • To develop and evaluate a novel computer vision model, the Dual-Branch Attention-Guided Vision Network (DBA-ViNet), for identifying and classifying diseases in multiple fruit types.
  • To compare the performance of DBA-ViNet against state-of-the-art pre-trained convolutional neural network (ConvNet) models.
  • To enhance the interpretability and trustworthiness of the model's predictions through visualization techniques.

Main Methods:

  • Utilized an open-source dataset of fruit disease images (apples, guavas, mangoes, pomegranates, oranges), split into training, validation, and testing sets.
  • Implemented 5-fold cross-validation to ensure model generalizability and stability.
  • Benchmarked Swin Transformer (ST), EfficientNetV2, ConvNeXt, YOLOv8, and MobileNetV3, and introduced the proposed DBA-ViNet with a dual-branch architecture for integrated feature extraction. Grad-CAM was used for visualization.

Main Results:

  • The DBA-ViNet model achieved superior performance, with a testing accuracy of 99.51%, specificity of 99.42%, recall of 99.61%, precision of 99.30%, and F1 score of 99.45%.
  • DBA-ViNet outperformed all benchmarked state-of-the-art models across all evaluation metrics.
  • Grad-CAM visualizations confirmed that DBA-ViNet accurately focuses on disease-specific symptoms, enhancing model transparency.

Conclusions:

  • The proposed DBA-ViNet architecture demonstrates high accuracy and reliability in fruit disease detection.
  • Integrating global and local feature extraction via a dual-branch attention mechanism is effective for classification tasks.
  • DBA-ViNet shows significant potential for practical application in smart agriculture and automated crop health monitoring systems.