Identification and classification of leaf diseases in agriculture are crucial for crop health. Traditionally, this task has been manual and error-prone. Recent advances in computer vision and machine learning, specifically Convolutional Neural Networks (CNNs), have enabled automated leaf disease detection and classification. Our approach utilizes image data of various diseases (e.g., powdery mildew, rust) collected from different crops.

1. Automated Disease Diagnosis in Crops
Seminar Guide
Dr. Pradeep N Ph.d.,
Professor,
Department of CS&E,
BIET, Davangere
VISVESVARAYA TECHNOLOGICAL UNIVERSITY
BAPUJI INSTITUTE OF ENGINEERING TECHNOLOGY, DAVANGERE
Department of Computer Science and Engineering
TECHNICAL SEMINAR-2024
ON
Technical Seminar Co-ordinators
Prof. Rahima and Prof. Madhu Hiremath
Asst. Professors,
Department of CS&E,
BIET, Davangere.
-Manoj Bhavihal(4BD20CS051)
Head of Department
Dr. Nirmala C R Ph.d.,
Professor and Head,
Department of CS&E,
BIET, Davangere.

2. Contents
• Abstract
• Introduction
• Objectives
• Methodology
• Implementation
• Screenshots
• Future Scope
• Conclusion

3. Abstract
Plant diseases affect the growth of their respective species, therefore their early
identification is very important. Automatic leaf disease detection and classification
systems allow farmers to better detect and manage crop diseases, potentially
increasing productivity and farm sustainability. Agriculture field has a high impact on
our life. Agriculture is the most important sector of our economy. Proper
management leads to a profit in agricultural products. Farmers do not have
expertise in leaf disease so they produce less production. Plant leaf diseases
detection is important because profit and loss depend on production. CNN is the
solution for leaf disease detection and classification. The main aim of this research
is to detect the plants leaf diseases. Plant leaf disease detection has a wide range of
applications available in various fields such as Biological Research and in Agriculture
Institute. Plant leaf disease detection is one of the required research topics as it
may prove beneficial in monitoring large fields of crops, and thus automatically
detect the symptoms of diseases as soon as they appear on plant leaves.

4. Introduction
Identification and classification of leaf diseases in agriculture are crucial for crop
health. Traditionally, this task has been manual and error-prone. Recent advances in
computer vision and machine learning, specifically Convolutional Neural Networks
(CNNs), have enabled automated leaf disease detection and classification. Our
approach utilizes image data of various diseases (e.g., powdery mildew, rust) collected
from different crops.
Types of Diseases:
Healthy Leaves: Vibrant green coloration, smooth texture, and uniform appearance.
Powdery Leaves: Characterized by a powdery white or gray substance on the surface.
Rusty Leaves: Exhibit rusty or reddish-brown lesions, often accompanied by yellowing.
CNN Architecture:
Convolutional Layers : Extract features like edges, textures, or shapes.
Pooling Layers : Reduce spatial dimensions while retaining important information.
Activation Functions : Introduce non-linearity for learning complex relationships.
Fully Connected Layers : Process high-level features and make predictions.
Output Layer : Produces final predictions, such as healthy or diseased.
Training Process: CNNs learn patterns and features relevant to leaf disease detection
by adjusting network parameters using labeled training data.
Prediction: Once trained, CNNs can make predictions on new data by propagating it
through the network and selecting the class with the highest probability.

5. Objectives
• To develop an automated system for the identification and classification of leaf
diseases in agriculture.
• To leverage recent advances in computer vision and deep learning to enhance the
efficiency and accuracy of leaf disease detection.
• To collect image data of various leaf diseases, including powdery and rusty
infections, from different crops.
• To design and implement a robust classification model capable of accurately
distinguishing between healthy leaves and leaves affected by powdery or rusty
infections.
• To evaluate the performance of the proposed method and compare it with
conventional approaches, demonstrating its superiority in terms of accuracy and
effectiveness in detecting and isolating different types of leaf diseases..

6. Methodology 1. Data Acquisition : Collecting
images of diseased and
healthy leaves from various
sources.
2. Preprocessing : Standardizing
the images by resizing,
cropping, and normalizing
them.
3. Splitting Data : Dividing the
dataset into training and
testing sets to train and
evaluate the model.
4. CNN Deep Learning Model:
Developing a Convolutional
Neural Network architecture
for leaf disease detection.
5. Predictions : Using the trained
model to make predictions on
new, unseen leaf images.

7. Screenshots

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11. Advantages
1. Automatic Feature Learning: CNNs can automatically learn
relevant features from raw image data without the need for
manual feature extraction. This ability to learn hierarchical
representations of features can lead to more robust and
accurate disease detection models.
2. Non-linear Decision Boundaries: CNNs are capable of learning
complex non-linear decision boundaries, allowing them to
capture intricate patterns and variations in leaf images that may
not be captured by linear classifiers like SVM.
3. Scale-Invariant Representations: CNNs can learn scale-invariant
representations of features, meaning they can detect diseases
across leaves of different sizes and shapes without the need for
explicit feature scaling or normalization.
4. Higher Accuracy: CNNs have demonstrated superior performance
in leaf disease detection tasks compared to kNN and SVM,
achieving higher accuracies and better generalization to unseen
data.

12. Model SVM KNN CNN
Accuracy 76% 64% 96%

13. Future Scope
1. Real-time Disease Detection on Mobile Devices : Developing lightweight and efficient
CNN models that can run on smartphones and other mobile devices. This allows farmers
to analyze diseases in their crops right in the field.
2. Integration with Drones and Robotics : Using CNN-based disease detection along
with drones for large-scale monitoring of fields. Robots can then be deployed to
intervene in affected areas autonomously.
3. Explainable AI for Disease Diagnosis : While CNNs are powerful, their decision-making
process can be hard to understand. In the future, we can use Explainable AI (XAI)
techniques to explain why the CNN identifies certain diseases. This helps build trust in
the system and provides insights into the features triggering disease identification.

14. Conclusion
Automatic leaf disease detection and classification systems offer significant potential
to revolutionize agriculture by enhancing crop disease control and mitigating their
economic and environmental consequences. Convolutional neural networks have
emerged as valuable tools for detecting and classifying various leaf diseases across
different plants and regions. While these machine learning models have demonstrated
remarkable success, their performance can be influenced by the availability and
quality of training data. Therefore, future research should prioritize the development of
methods to acquire and enhance training data to enhance the accuracy and efficacy of
these models. In conclusion, ongoing research in leaf disease detection and
classification is crucial for the sustainability and advancement of agriculture. By
refining accurate and efficient methods, we can empower farmers to better manage
crop diseases, thereby minimizing their economic and environmental impact and
ensuring food security for the future.

15. Thank You!