Abstract

Potato crops are vulnerable to various diseases, notably early blight and late blight, which can significantly reduce yield and quality. Traditional methods of disease detection are time-consuming and often require expert knowledge. This project presents a deep learning-based solution utilizing Convolutional Neural Networks (CNNs) to automatically detect and classify potato leaf diseases from images. By deploying this model through a user-friendly web interface, the system aims to assist farmers and agronomists in timely disease identification, facilitating prompt and effective intervention strategies.

Introduction

Agriculture plays a pivotal role in the global economy, with potatoes being one of the most widely cultivated crops. Diseases like early blight and late blight pose substantial threats to potato production, leading to economic losses and food insecurity. Early detection is crucial for effective disease management. Leveraging advancements in deep learning, particularly CNNs, this project endeavours to automate the detection process, making it accessible and efficient for end-users.

Problem Statement

Manual identification of potato leaf diseases is labour-intensive and prone to human error. There is a pressing need for an automated, accurate, and scalable solution that can assist in the early detection of these diseases to mitigate crop losses and ensure food security.

Existing System and Disadvantages

Traditional methods rely heavily on manual inspection and expert consultation, which are:

• Time-consuming: Delays in diagnosis can lead to disease proliferation.
• Subjective: Prone to human error and inconsistent assessments.
• Resource-intensive: Requires trained personnel and laboratory facilities.

Proposed System and Advantages

The proposed system employs a CNN-based model trained on labelled images of potato leaves to classify them into:

• Healthy
• Early Blight
• Late Blight

Advantages:

• Automation: Reduces the need for manual inspection.
• Accuracy: High precision in disease classification.
• Accessibility: Web-based interface allows for widespread use.
• Scalability: Can be extended to other crops and diseases.

Modules

1. Data Collection and Pre-processing: Gathering and preparing image data for training.
2. Model Development: Designing and training the CNN model.
3. Web Interface: Developing a user-friendly platform for image upload and result display.
4. Deployment: Hosting the model and interface on a server for public access.

Algorithm:

The system utilizes a Convolutional Neural Network (CNN) architecture, which includes:

• Convolutional Layers: Extract features from input images.
• Pooling Layers: Reduce spatial dimensions and computation.
• Fully Connected Layers: Perform classification based on extracted features.
• Activation Functions: Introduce non-linearity (e.g., ReLU).
• Softmax Layer: Outputs probability distribution over classes.

Software and Hardware Requirements

Software:

• Programming Language: Python
• Libraries: TensorFlow/Keras, NumPy, OpenCV
• Web Framework: Flask or Django
• Deployment Platform: Vercel or Heroku

Hardware:

• Development: System with GPU support for model training
• Deployment: Cloud server with sufficient resources to handle user requests

Conclusion

The “Detect-Potato-Disease” project demonstrates the efficacy of deep learning in agricultural disease detection. By automating the identification process, it offers a rapid, accurate, and accessible tool for farmers and agricultural professionals, potentially reducing crop losses and improving yield quality.

Future Enhancements

• Mobile Application: Develop a mobile app for on-field disease detection.
• Expanded Dataset: Incorporate more diverse images to improve model robustness.
• Multi-Disease Detection: Extend the model to detect other crop diseases.
• Real-time Monitoring: Integrate with drone or satellite imagery for large-scale monitoring.