Machine Learning : Modern Tool in Plant Disease Diagnosis

1. Doctoral seminar-I
Machine Learning : Modern Tool in Plant
Disease Diagnosis
Presented by -
Prince Kumar Gupta
Ph.D (Ag.) student
G.B.P.U.A.& T, Pantnagar

2. Contents
Importance of Plant Disease
Diagnosis of Plant Pathology
Artificial Intelligence
Introduction of Machine
Learning
Steps in M.L algorithm
Applications of M.L
Deep Learning
M.L based Mobile APPs
Case studies
Future aspects and Conclusion

3. Importance of Plant Disease
Plant disease causes substainable
looses in yield, leading to huge
economic losses.
Not only hamper the productivity,
but also affect the human and
livestock health
It exploit plant, resulting in low productivity
which acts as threat for food and nutritional
security.
Rekha et al.,2017

4. Diagnosis of plant disease is to identify the disease
and to determine the causal agent (Pathogen).
Diagnosis of Plant Disease
Traditional method
of identification
–Visual examination.

5. Laboratory
1 Soil analysis and measurement of pH
2 Biochemical method
3 Microscopic examination
4 Serological methods
5 Use of methods rely on DNA
Direct
PCR
FISH
ELISA
Immunoflurescence
Flow cytometry
Indirect
Thermography
Florescence imaging
Hyperspectral Technique
Artificial intelligence
Current Prospectives:
Fang et al.,2015

6. Artificial Intelligence
1956, John Mc Carty
‟The science and
engineering of making
intelligent machine”
System able to perform
task that require human
intelligent-
Visual perception,
Speech recognition, face
recognition and
Translation of languages
AI: Not a system ,
AI – Implemented in system.

9. Key Terminologies
Data into information
Problem solving tool
Combination of CS engineering and
Statistics
Interpretet data and act on it
Enhance performance using past
experiences

10. what?
Learning is any process by which a system improves
performance from experience
Term Machine learning was coined in 1959 by Arthur Samuel.
Science of making computer learn and act like human by feeding
data and information without being explicitly programmed
The subset of Artificial Intelligence that work automatically or
give the instructions to a particular system to perform a action
and enhanced performance from past experience.
Goal :
Understand the structure of given data and fit data into models
that can be understood and utilized by the people.
Alex et al.,2008

11. Traine the
system by
feeding data
Analyse the
data
Result
Machine learning makes prediction and
decision based on past data
System learn (algorithm)
Data input
(for testing)
images

12. Why machine learning?
Control: Varieties of
fungicides are available to
control diseases.
Crop disease
(presence of disease mainly
reflected by symptoms on
leaves )

13. Need: Automatic, accurate and
less expensive Machine Vision
System for detection of diseases
from the image and to suggest a
proper management
For accurate plant disease
diagnosis and effective chemicals-
a difficult task - requires experts
advise - time consuming and
expensive

14. Why
now ?
Flood of available
data
Increasing
computational
power
Progress in
available
algorithms
&
Theory developed
by researchers
Increasing
support from
industries
Why
now ?

15. Steps in Machine Learning
1. Collection of
data (images)
2. Tabulation
3. Data
Prepration
High quality
Training of data
Clean data
Organize data
Elimination
Create high
quality data

16. Steps in Machine Learning
4. Data Input
5. Data
Processing
6. Data Output
First stage - raw
data begins - form
usable information.
Machine learning
algorithm
In form of images,
text etc.

17. 8. Data
Storage
7. Prediction
Alex et al.,2008
Database

18. Types of Machine Learning algorithm
Supervised learning
1. Training model
2. Both input
(image) +Visual
symptoms
4. Naire Baye
algorithm
3. Classsification Learning algorithm
Model
Blast of rice having
specific disease
symptoms
Linear Regression
Nearest Neighbor
Guassian Naive Bayes
Decision Trees
Support Vector Machine
(SVM)
Random Forest
Result

19. Unsupervised learning
1. Only input
(images)
3. K-mean
algorithm
2. Clustering
(on the basis of
features)
result

20. Reinforcement learning algorithm
Data (image)
Model using
algorithm
Result save in
database
For detection test
If result given by model is
same as data already store in
model database then its
means model is accurant and
rewarded as improving
efficiency .

22. Application in Agriculture
Data aggregation- water
uses in irrigation.
Signal sensor is used viz;
GSM and soil sensor.
Soil sensor- provides
moisture, and water level
regasrding informations.
GSM- Collects weather
data information

23. Through machine
learning software,
drone can adopt to
learn to navigate
better research and
evaluates crop and
livestock based on
the data that
already feed.

24. Robocrop: smart robert
for picking fruits
Crop monitoring
system

25. Animal identification and Health
monitoring.

26. Alibaba and Dekon
By observing movement of pig/day - evaluate health of particular pig.
By recording sound of pig cough they monitor the spread of disease.
By use of machine learning, the mortality of piglet reduced.

27. Controlling Greenhouse Climate
(Liakos et al.,2018)

28. Handling huge amount
of
data
Complex Problems
Feature Extraction

29. Deep learning....
Deep learning is a subset of machine learning
Based on Artificial Neural Network
Inspired by the Biological Neural Network
Different algorithm are used most common
Convolution Neural Network (CNN)

30. Convolutional Neural Network
CNN commonly applied to analyzing visual imagery using neurons.
Applicable in image recognition and image classification using filters.
Convolution Pooling
Scanning

31. Input (image number, width, height) identfy by machine in form of pixel (having
image value)and these are then scan by filter (convolution layer).
Filter are mainly of different type: color, edge,corner filter
And of different size with some random values.
convolution convolution
pooling pooling

40. Methodology
Collection of image:
Healthy: 95,
Bscterial blight: 125,
Blast: 170 ,
Sheath rot: 110 and
Brown spot: 150.

41. Pre processing
stage……..
Image are resize and
cropped into pixel (300 x
450)
Only leaf portion with the
disease part is present in
image.

42. K-mean clustering based
segmentation
Feature extraction
Color Texture

43. Experimental results

44. Accuracy (%) when model implement……
Normal : 90.57; Bacterial blight: 95.78; Blast: 98.9; Brown
spot: 94 and Sheath rot: 92

47. Layer Type Filter size Output size
L 1 CONV 3X3 128X128X32
POOL 2X2 64X64X32
L 2 CONV 4X4 61X61X64
POOL 2X2 64X64X32
L 3 CONV 1X1 30x30x128
POOL 2X2 15x15x128
Training accuracy (80%) Test accuracy (20%)
99.21 % 99.32%
Precision performance of the model
Healthy 1.0
Septorial leaf blight 0.99
Frogeye 0.99
Downy mildew 0.98
Result

48. Linking machine learning with DSS (Decision Support
System) for better advisory service for farmer.
Image processing and spreading the usage of the model by
training it for plant disease recognition on wider land areas.
Severity of the
disease
Price list for
the pesticides

49. Conclusion…..
Automatic, Speed and Accuracy.
Innovative, efficient & fast
interpreting algorithms help in plant disease detection
Bridge the gap between experimentation
and real life application.