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Explainable deep learning-based comparative study for guava fruit and leaf disease classification: advancing

¹School of Economics, Shandong Normal University, Jinan, Shandong, China.

Frontiers in Plant Science

|May 14, 2026

Summary

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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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This study introduces an explainable deep learning framework for early guava disease detection. The VGG16 + MobileNetV2 hybrid model achieved 96% accuracy, offering reliable AI-assisted plant disease diagnosis for sustainable agriculture.

Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Early detection of guava diseases is crucial for preventing significant yield losses and ensuring agricultural sustainability.
Deep learning models offer potential for disease identification but face challenges in accuracy and interpretability for practical use.

Purpose of the Study:

To develop an explainable deep learning framework for classifying guava fruit and leaf diseases.
To enhance the accuracy and interpretability of AI models for agricultural disease diagnosis.

Main Methods:

A dataset of 527 annotated guava images across five classes was used.
Six hybrid deep learning models were created by combining transfer learning backbones (VGG16, MobileNetV2, InceptionV3, ResNet50) with custom CNN classifiers.

Keywords:

Grad-CAM deep learning explainable AI guava disease classification hybrid CNN plant pathology smart agriculture transfer learning

Related Experiment Videos

Gradient-weighted Class Activation Mapping (Grad-CAM) was used for model interpretability.

Main Results:

The VGG16 + MobileNetV2 hybrid model achieved the highest performance with 96% accuracy and an F1-score of 0.96.
The model demonstrated strong generalization across all disease classes.
Comparative analyses confirmed the superior performance of the proposed hybrid model.

Conclusions:

Combining deep feature extractors with lightweight architectures improves classification accuracy and efficiency.
Explainable AI techniques like Grad-CAM increase trust and interpretability in AI-driven disease diagnosis.
The framework shows potential for real-time smart farming and mobile diagnostic applications, especially in resource-limited settings.