Classification of Plant Leaf Diseases Using ResNet18 Enhanced with Inception and Capsule Network

Abstract

Accurate and early detection of plant leaf diseases is crucial for ensuring crop health and improving agricultural productivity. This work proposes a hybrid deep learning model that combines ResNet18, Inception blocks, and fully connected Capsule layers to classify leaf images of apple, grape, and corn plants into healthy or diseased categories. ResNet18 is used as the backbone for deep feature extraction, while Inception modules enhance the network’s ability to capture multi-scale patterns. Capsule layers are employed at the final stage to retain spatial relationships and pose information, improving the model\'s ability to recognize complex disease features. The model is trained and evaluated using images from the PlantVillage dataset, with separate configurations for each crop. The proposed model achieved validation accuracies of 99.84% for apple, 100% for grape, and 97.27% for corn. Performance is further assessed using precision, recall, and F1-score, and compared against a baseline ResNet18 model. The results demonstrate that the proposed architecture significantly improves classification accuracy and feature understanding, making it a strong candidate for real-world agricultural disease monitoring systems.

Introduction

Agriculture is crucial for the global economy, especially in countries like India. However, crop productivity is often threatened by plant diseases, particularly leaf diseases, which are common and visually identifiable. Traditional disease detection methods (manual inspection by experts) are not scalable or fast enough. This has led to the development of AI-based automated detection systems, especially using deep learning (DL) models like Convolutional Neural Networks (CNNs).

2. Motivation & Limitations of Traditional CNNs

CNNs (e.g., ResNet, VGG, GoogLeNet) are widely used for image classification but face limitations in:

• Capturing spatial hierarchies

• Generalizing across visually similar diseases

• Preserving orientation and feature relationships

To address these issues, this work proposes a hybrid deep learning architecture that integrates:

• ResNet18 – deep feature extraction via residual connections.

• Inception blocks – for multi-scale feature extraction.

• Capsule Networks – to retain spatial relationships and orientation of features.

3. Literature Review Highlights

• Hosny et al.: Hybrid CNN + LBP features; 98–99% accuracy on Apple, Grape, Tomato.

• Madhurya & Jubilson: Advanced pipeline using CLAHE, YOLOv7, and ShuffleNetV2 with optimization; 99.69% accuracy.

• Moupojou et al.: FieldPlant dataset from real-world farms; MobileNet outperformed others.

• Balafas et al.: Review of 79 studies; ResNet50 and YOLOv5 were top performers.

• Benfenati et al.: Unsupervised anomaly detection via autoencoders; 90.35% accuracy.

• Hama et al.: Improved ResNet50 with selective layer freezing; 99% accuracy.

• Babu et al.: DenseNet-121 and ResNet101 best for rice disease detection.

• Maurya et al.: RAI-Net (ResNet18 + Inception + channel attention); 97.88% accuracy.

• Noon et al.: Modified YOLOX for detecting co-occurring cotton diseases.

• Theerthagiri et al.: SqueezeNet for maize diseases; 97% accuracy and real-time performance.

4. Proposed Model Overview

A hybrid deep learning model combining:

A. ResNet18 Backbone

• Extracts hierarchical features.

• Uses skip connections to avoid vanishing gradients.

• Lightweight yet effective for deep learning tasks.

B. Inception Blocks

• Extracts multi-scale visual patterns using 1x1, 3x3, 5x5 convolutions.

• Placed after ResNet to improve feature diversity.

C. Capsule Layers

• Represent features as vectors instead of scalars (pose-aware).

• Retains spatial relationships (e.g., orientation, size, position).

• Uses a fully connected capsule approach (simpler than dynamic routing).

D. Classification Layers

• Final fully connected layer → Softmax → Output.

• Outputs the probability distribution across disease classes.

5. Architecture Summary

1. Input leaf image.

2. Processed by ResNet18 for deep feature extraction.

3. Features fed to Inception block for multi-scale analysis.

4. Resulting features go into Capsule layer to preserve spatial structure.

5. Final classification based on vector outputs via Softmax.

6. Experimentation: Datasets Used

Used the PlantVillage dataset for training and testing:

• Apple: 3,171 images across 4 classes (Scab, Black Rot, Rust, Healthy).

• Grape and Corn datasets (not fully detailed in excerpt but implied).

• Images are standardized (e.g., 256x256 pixels, plain backgrounds).

• Diseases show distinct visual symptoms for classification training.

? Key Advantages of the Proposed Model

• Generalizable across multiple crops and diseases.

• Modular and adaptable (easy to expand for new crops/diseases).

• Works well in real-world agricultural scenarios.

• Can be deployed in mobile apps, drones, or edge devices.

Conclusion

The proposed ResNet18 model enhanced with Inception blocks and a fully connected Capsule Network achieved good results for plant leaf disease classification in apple, grape, and corn datasets. By integrating residual learning, multi-scale feature extraction, and spatially aware capsule encoding, the model effectively captured disease-specific patterns and obtained higher accuracy than the baseline ResNet18. Experimental evaluation showed that the proposed model achieved 99.84% accuracy for apple, 100% for grape, and 97.27% for corn, outperforming the standard ResNet18 across all metrics. In addition, the precision, recall, and F1-scores are consistently above 93%, confirming the robustness of the approach. These outcomes highlight the effectiveness of combining deep learning architectures for reliable agricultural disease detection, supporting timely intervention to safeguard crop yield and minimize economic losses. For future work, the model can be extended to larger and more diverse datasets collected under real field conditions, with further improvements such as attention-based feature refinement, optimized capsule routing, and lightweight deployment on mobile or edge devices to enable real-time disease detection for precision agriculture.

References

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Copyright

Copyright © 2025 Vemuluri Divya, Durga Ganga Rao Kola. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.