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1. Project
presentation
Presented by: Pranjay Saxena
Mohd. Afzal
Plant Disease
Detection
System

2. Origin of the
creative idea
• Advanced Plant Disease Detection System:
Uses image processing and machine learning
algorithms.
• User-Friendly: Designed for farmers,
agronomists, and researchers with an intuitive
interface.
• Quick & Accurate: Provides reliable diagnoses
from uploaded plant images.
• High Accuracy: Reduces errors and improves
disease management.
• Benefits: Enhances crop protection, boosts
productivity, and enables faster responses to
plant health issues.

3. Project
Vision
Project
Mission
• Develop and deploy a state-of-the-art Plant
Disease Detection System.
• Leverage cutting-edge technologies such as
image processing and machine learning.
• Enable rapid, accurate, and reliable diagnoses of
plant diseases.
• Contribute to healthier crops and reduced
losses.
• Promote more effective disease management in
agriculture.
• Revolutionize plant health monitoring.
• Provide an advanced, easy-to-use, and
highly accurate system.
• Empower farmers, agronomists, and
researchers to detect and manage plant
diseases efficiently.
• Ensure sustainable agricultural practices.
• Enhance crop productivity.

4. • Rising agricultural challenges, including plant diseases, climate change, and pests,
reducing crop yields and sustainability.
• Technological advancements, particularly in image processing and machine learning,
to address complex agricultural issues with precision.
• The need for global food security, aiming to enhance food production for a growing
global population while promoting sustainable practices.
• The success of precision agriculture and the increasing use of data-driven solutions
to improve farming efficiency and productivity.
• Innovative disease detection by combining AI and Deep Learning for early plant
disease diagnosis through images, making it accessible for all users.
• User-centric design with an intuitive, easy-to-use interface for farmers to upload
images and get instant diagnoses without needing specialized knowledge.
• Customization to work with diverse crops and regions, adapting to various plant
diseases and environmental conditions.
• Real-time data analysis, enabling immediate disease management and minimizing
Inspiration :
Creativity :

5. Ideation process
01
02
03
04
Generating Ideas:
• Identify challenges like early
detection, ease of use, and accuracy.
• Explore technologies like image
processing, machine learning, and AI.
• Focus on solutions accessible to users
with varying expertise.
Brainstorming:
• Gather insights from agricultural experts
and users.
• Brainstorm features like real-time
diagnosis, multi-crop compatibility, and
mobile access.
• Consider integration with other
agricultural tools.
Developing Ideas:
• Develop concepts for system design
and functionality.
• Create user-friendly interfaces for
image uploads.
• Build accurate machine learning
algorithms for disease detection.
Refining Ideas:
• Test concepts with user groups
gather feedback.
• Optimize algorithms for accuracy
speed.
• Refine the interface for improved u
experience and compatibility.

6. Creation process
1.Dataset Collection:
⚬ Collected a dataset from Kaggle containing images of various plant diseases.
2.Data Augmentation:
⚬ Applied data augmentation techniques to increase the diversity of the dataset, such
rotating, flipping, and scaling images to improve model generalization.
3.Data Preprocessing:
⚬ Preprocessed the images by resizing them to a uniform size, normalizing pixel value
and ensuring the data is in the correct format for input into the model.
4.Model Training (Initial CNN Application):
⚬ Applied Convolutional Neural Networks (CNN) to the preprocessed dataset for initial
training.
⚬ Found that the model achieved around 80% accuracy on the validation set.
5.Model Improvement:
⚬ Modified the model by adjusting the learning rate to improve training efficiency and
model performance.
⚬ Increased the number of epochs to allow the model to learn more effectively.
⚬ Implemented Early Stopping to prevent overfitting and ensure optimal performance
during training.
6.Model Evaluation and Refinement:
⚬ Continued evaluating the model's performance on validation data and refined the
hyperparameters as necessary to improve accuracy further.

7. Model
Metrics

9. Weaknesses Threats
Strengths Opportunities
• Data quality
• Computational cost
• Image quality
• Limited disease
types
• Data privacy
• Model complexity
• Environmental factors
• Emerging diseases
• Technological
limitations
• Precision agricultur
• Real-time monitoring
• Mobile applications
• Global impact
• Collaboration with
experts
• Early detection
• Reduced pesticide use
• Increased crop yield
• Cost-effective
• Remote monitoring

10. Final reflections:
• Model Performance: Evaluate model's accuracy, precision, recall, and
F1-score.
• Computational Efficiency: Optimize model for faster inference time.
• Data Quality and Quantity: Prioritize high-quality and diverse training
data.
• User Interface: Develop user-friendly interface for easy image input and
diagnosis.
• Explore advanced CNN architectures for better performance.
• Develop a mobile app for farmer accessibility.
• Collaborate with experts for refined recommendations.
• Incorporate environmental factors for comprehensive analysis.
• Implement robust data privacy measures.
• Gather user feedback for continuous improvement.
• Stay updated with latest advancements in deep learning.
• Promote technology adoption through community engagement.
Future
Steps:

11. Thank you
very much!