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Automated Guava Disease Detection Using Transfer Learning With ResNet-101.

Muhammad Ahmed¹, Fahad Ahmed¹, Naila Sammar Naz¹

¹School of Computer Science National College of Business Administration and Economics Lahore Pakistan.

Food Science & Nutrition

|December 24, 2025

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This study introduces an AI-powered system using deep learning (DL) and ResNet-101 for automated guava disease detection. The model achieves 98.48% accuracy, offering a sustainable solution for modern agriculture.

Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Manual disease identification in agriculture is inefficient, time-consuming, and error-prone.
Sustainable agriculture demands automated, accurate disease detection technologies.
Guava cultivation faces challenges with disease identification impacting yield and quality.

Purpose of the Study:

To develop and evaluate a deep learning (DL) model for automated guava disease detection.
To leverage transfer learning (TL) with ResNet-101 for enhanced classification accuracy.
To provide an interpretable and transparent AI solution for plant disease identification.

Main Methods:

Utilized ResNet-101 architecture for direct image analysis without manual feature extraction.

Keywords:

ResNet‐101 deep learning explainable AI guava disease detection transfer learning

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Applied data augmentation to a dataset of 3784 images, increasing it to 4632 balanced images.

Preprocessed data using normalization and resizing; split into 80% training, 10% validation, and 10% testing sets.

Employed Gradient-weighted Class Activation Mapping (Grad-CAM) for visualization and interpretability.

Main Results:

Achieved an impressive classification accuracy of 98.48% for guava diseases (Anthracnose, Fruit Fly, Healthy).
Model performance was evaluated using nine key metrics including precision, recall, F1 score, and specificity.
Grad-CAM visualizations confirmed the model's focus on relevant diseased areas, ensuring transparency.

Conclusions:

Deep learning with ResNet-101 offers a highly accurate and efficient method for automated guava disease detection.
The developed AI technology is a viable and scalable solution for large-scale agriculture and sustainable farming practices.
The interpretable nature of the model enhances trust and applicability in real-world agricultural settings.