Introduction

The Plant Disease Detection System is designed to assist farmers and agricultural experts in identifying diseases in crops at an early stage. Using advanced machine learning techniques, this system can detect specific plant diseases with high accuracy, helping to minimize crop losses and optimize agricultural output.

How It Works

The system utilizes a multi-stage approach to ensure reliability:

1. Image Input: The system allows users to upload an image of a fruit or vegetable plant.
2. Disease Detection:
   • The uploaded image is first processed using a pre-trained machine learning model to verify if the image is of the target plant.
   • If confirmed, a second model is used to detect any signs of plant diseases.
3. Result: The system returns the most likely diagnosis along with a confidence score, enabling users to take informed action.

Development Process

Data Collection and Preparation

Images of various fruits and vegetable plants were collected from diverse sources, including farms, research datasets, and online repositories. Both healthy and diseased samples were included to ensure comprehensive coverage.

• Data Cleaning: Irrelevant, blurry, or duplicate images were removed.
• Data Augmentation: Techniques like rotation, flipping, and color adjustment were applied to augment the dataset and prevent overfitting.
• Class Balancing: Class imbalance was addressed by oversampling underrepresented classes, ensuring rare diseases are detected accurately.

Model Building and Training

A convolutional neural network (CNN) was designed to process image data efficiently. The architecture was tailored for high accuracy while maintaining computational efficiency.

• Pretrained Model: The system uses an EfficientNet model, pre-trained to handle image classification tasks efficiently.
• Training and Testing: The dataset was split into training, validation, and testing subsets. The model was trained using hyperparameter optimization and fine-tuned to monitor performance.

Model Results and Performance

The results demonstrate the robustness of the system:

• Accuracy Progress: The model started with an accuracy of 82.76% after the first epoch and reached 99.57% by the 10th epoch.
• Testing Results: The model achieved a perfect 100% accuracy on the test data.
• Key Metrics: For each disease, it scored nearly perfect precision, recall, and F1-scores.

"Even rare diseases with fewer training samples, like Anthracnose Fruit Rot, were identified perfectly."

This system proves that common and rare cases alike can be classified flawlessly, offering a powerful tool for modern agriculture.