leave disease detection using machine learning.

1. smart farming
LEAF DISEASE DETECTION using
machine learning
An Analysis of SVM and CNN Approaches
Name Sem Branch
THARUNESHARA PRASAD S VII E & C E
POOJA G L VII E & C E
SANDEEPA P VII E & C E
CHINMAYI M.R VII E & C E
MADHUSUDHAN G S VII C & S E

2. • The main function of a leaf is to produce food for the plant by photosynthesis.
• They also store food and water, and function the loss of water vapor from the
plant to the atmosphere.
• Using plant leaves to detect diseases is a suitable, reliable, and efficient method
for detecting diseases in plants
• By viewing the plant leaves symptoms, the detection of diseases automatically
makes them more accessible and cheaper.

3. • A major factor in India's economic growth is agriculture.
• In order to increase food production, the agriculture sector looks for
efficient ways to protect crops.
• Disease detection of leaf is critical for a productive farming system.

4. • Current agriculture lacks a timely and accurate leaf disease detection
system, impacting crop health and productivity.
• A user-friendly solution, utilizing machine learning for precise
classification across various crops, is essential for proactive intervention
and sustainable farming practices.

5. Early Disease Detection
Create a strong system for detecting
plant leaf diseases early
1
2 High Accurate MLAlgorithms
Apply SVM and CNN for accurate leaf
classification.
3 Dataset diversity & realism
Get Kaggle dataset for effective crop leaf
and disease training.
4 Practically applicability
Design a farmer-friendly system for
precision agriculture integration.

6. Identification of
Plant-Leaf
Diseases Using
CNN and Transfer
Learning
Author Name :Sk
Mahmudul
Hassan
Year:
2021
Plant Disease
Detection and
Classification by
Deep Learning
Author Name:
BIN WANG
Year:
2021
Improvement of
Crop Production
Using
Recommender
System by Weather
Forecasts
Author Name :
S. Bangaru Kamatchi
Year:
2020
Intelligent Crop
Recommendation
System using
Machine Learning
Author Name : Dr
Subba Reddy
Borra
Year:
2022

8. Classification
Feature extraction
Image segmentation
Image preprocessing
Input Image

9. Feature
Extractio
n
Utilize GLCM for
color, shape, and
texture feature
extraction
SVM Classifier
High-dimensional
spaces, memory
efficiency using
support vectors
CNN
Model layers include
input, convolution,
pooling, fully
connected, and
output layers.

10. Support Vector Machines (SVMs):
• SVM are supervised learning algorithms that can be used for classification or regression tasks.
• The main idea behind SVMs is to find a hyperplane that maximally separates the different classes
in the training data.
• SVMs are particularly useful when the data has many features, or when there is a clear margin of
separation in the data.
Convolutional Neural Network(CNN):
• A convolutional neural network, or CNN, is a deep learning neural network designed for
processing structured arrays of data such as images.
• Convolutional neural networks are widely used in computer vision and have become the state of
the art for many visual applications such as image classification.

11. • The samples of tomato leaf of village dataset is considered to carry out to
evaluate the proposed model.
• The model is validated by training/testing the dataset samples. The
hardware and software specifications are recommended to carry out the
work.
• The result of proposed model is compared with existing models. It is
observed that, the accuracy of Proposed model (SVM+CNN)

12. Disease detection done using image processing
and ML algorithms
Leaf disease portion segmented, features
extracted with CNN.
SVM used for recognition, achieving 80%
accuracy

13. • Mrs.Shruthi U, Dr.Nagaveni V, Dr.Raghavendra B K, “A Review on Machine
Learning Classification Techniques for Plant Disease Detection,” ICA
CCS, IEEE, 2019
• Melike Sardogan, Adem Tuncer, “Plant Leaf Disease Detection and Classification
based on CNN with LVQ Algorithm,” IEEE, 2018
• Sachin D. Khirade, A. B. Patil, “Plant Disease Detection Using Image
Processing,” IEEE, 2015

15. hank
Yo