Identifying and Categorizing Plant Diseases Through Deep Learning Techniques

Abstract

Using a 2D Convolutional Neural Network (CNN) that uses prediction and classification techniques, we describe a novel method for automated tomato leaf disease detection in this work. Our dataset includes a wide range of classes, from healthy leaves to leaves with common diseases as bacterial spot, target spot, tomato leaf curl virus, blight, and septoria leaf spot. We carefully choose the dataset using methodical pre-processing and augmentation procedures to enable efficient training, validation, and testing. The CNN design combines convolutional and pooling layers with fully linked layers for accurate classification, effectively extracting significant characteristics from leaf images. By training the algorithm on this improved dataset, we are able to classify and predict tomato leaf diseases with amazing accuracy.

Introduction

Introduction:
Tomato leaf diseases threaten crop yield and food security. Traditional manual disease identification is time-consuming and error-prone. This study presents an automated, cost-effective method using 2D Convolutional Neural Networks (CNNs) to detect and classify tomato leaf diseases via image analysis. This deep learning-based solution enables early diagnosis, which is crucial for timely disease management.

Key Components:

1. Background on Plant Diseases:

• Crop diseases, caused by pathogens like fungi, bacteria, and viruses, lead to major agricultural losses.

• Visual inspection by experts is traditionally used but is inefficient.

• CNNs have emerged as powerful tools in plant disease detection, improving accuracy and reducing subjectivity.

2. Deep Learning Overview:

• Deep learning mimics the human brain through artificial neural networks.

• It is especially impactful in computer vision, enabling models to learn features from image data autonomously.

3. Literature Review Highlights:

• Several studies demonstrated the success of CNNs and transfer learning (e.g., ResNet, DenseNet, EfficientNet) for plant disease classification.

• Limitations include dataset quality, background complexity, and the need for real-world conditions.

• Approaches such as data augmentation, GAN-generated synthetic images, ensemble models, and federated learning were explored to address issues like class imbalance, privacy, and overfitting.

4. Methodology:

The proposed system consists of:

A. Data Loading:

• A curated dataset with multiple tomato leaf classes (e.g., healthy, blight, bacterial spot, curl virus, septoria).

B. Data Preprocessing:

• Techniques include resizing, normalization, and cleaning to improve data quality and prepare it for training.

C. Feature Extraction:

• Relevant image features are selected using tools like PCA, enhancing the model’s ability to distinguish between disease types.

D. Model Training and Testing:

• The 2D CNN model is trained and validated on the processed dataset using standard practices such as data splitting, optimization, and performance monitoring.

E. Prediction and Evaluation:

• The trained model classifies new leaf images into disease categories.

• Performance is evaluated using metrics like accuracy, precision, recall, and F1-score, often compared to expert-labeled ground truth.

5. Results and Performance:

• The 2D CNN achieved high accuracy in identifying several tomato diseases and distinguishing them from healthy leaves.

• The model proved robust in real-world scenarios thanks to effective preprocessing and training.

• This system provides a reliable, scalable tool for automated plant disease detection, enabling early intervention and reduced crop losses.

Conclusion

In conclusion, the automated identification of tomato leaf diseases using a 2D Convolutional Neural Network (CNN) represents a significant advancement in agricultural technology. In addition to effective CNN model training and testing, meticulous feature selection, preprocessing, and dataset curation achieve remarkable accuracy in sickness prediction and categorization. This innovative method may assist lower crop losses and increase agricultural productivity by offering a practical choice for the early detection and management of tomato leaf diseases.

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Copyright

Copyright © 2025 A. Thamaraiselvi, S. Lesa, C. Poongodi. 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.