Plant Diesease Detection by Leaf Image Classification using Convolutional Neural Network

Abstract

This paper presents a comprehensive MATLAB-based deep learning framework for early detection and classification of plant diseases using Convolutional Neural Networks (CNNs). Our approach processes plant leaf images through a systematic pipeline consisting of acquisition, preprocessing, segmentation, and feature extraction. We apply a custom CNN model to classify leaves into various disease categories and a healthy class. The proposed system demonstrates high accuracy and scalability, promoting precision agriculture and sustainable farming through timely diagnosis and intervention.

Introduction

I. Introduction

Plant diseases cause over 30% loss in crop yield annually, impacting global food security and farmers’ income. Traditional disease detection relies on manual inspection, which is prone to human error and lacks scalability, especially in rural areas.

To overcome this, Convolutional Neural Networks (CNNs)—a form of deep learning—offer a powerful alternative by automating plant disease recognition from leaf images.

II. Literature Review

Various techniques have been explored for disease detection:

Deep learning methods outperform traditional ML techniques like KNN and SVM, which depend on handcrafted features.

III. Methodology

The system follows a structured pipeline:

1. Image Acquisition: Capture leaf images using mobile devices or drones.

2. Preprocessing: Resize, normalize, and augment images to improve model performance.

3. Feature Extraction: CNN learns visual patterns (e.g., spots, color changes).

4. Classification: Uses a softmax layer to assign disease classes.

5. Output: Displays disease name and prediction confidence.

IV. CNN Architecture (MATLAB)

V. Implementation

• Environment: MATLAB R2023a

• Dataset: 3000 images, 6 classes (healthy + 5 diseases)

• Split: 80% training, 20% testing

• Optimizer: SGD with momentum

• Loss Function: Cross-entropy

• Training: 50 epochs, batch size 32

VI. Results and Performance

The trained CNN achieved:

• Test Accuracy: 91.2%

• Precision & Recall: >90% for most disease categories

Conclusion

Conclusion: The proposed work demonstrates the feasibility of using deep learning, specifically CNNs, for effective plant disease detection via leaf image classification. Key conclusions drawn from the research include: 1) The CNN-based system achieves over 91% accuracy, validating its capability to identify multiple plant leaf diseases efficiently. 2) Image preprocessing and augmentation significantly enhance model performance by improving generalization. 3) MATLAB proves to be an effective platform for developing, training, and testing deep learning models with user-friendly interfaces. 4) Automated detection can reduce dependency on human expertise and help mitigate the risk of crop loss due to delayed identification. 5) CNNs outperform traditional machine learning algorithms (e.g., SVM, KNN) by extracting hierarchical features automatically. 6) The model shows strong precision and recall across different disease classes, proving its robustness even with a limited dataset. 7) The use of color-space transformations (RGB to HSI) and segmentation techniques improves background noise elimination. 8) The proposed approach supports the vision of smart agriculture and provides a scalable solution for field deployment. 9) The confusion matrix and performance metrics confirm the reliability of the system in real-time scenarios. 10) The approach lays the foundation for building fully automated, data-driven plant health monitoring systems.

References

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Copyright

Copyright © 2025 Aniket Ashok Rothe, Sanket Datta Rasal, Omkar Ramesh Sanap, Prof. S. K. Choudhary. 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.