APPLICATION OF ARTIFICIAL INTELLIGENCE TO TRACK PLANT DISEASES

APPLICATIONS OF ARTIFICIAL
INTELLIGENCE TO TRACK PLANT DISEASES
MASTER’S SEMINAR - II
ABHISEK RATH
I.D. NO. - PGS19AGR8175
DEPARTMENT OF PLANT PATHOLOGY
COLLEGE OF AGRICULTURE

CONTENT
INTRODUCTION 01
WHY ARTIFICIAL INTELLIGENCE? 02
DEVELOPMENT OF ARTIFICIAL INTELLIGENCE 03
CHARACTERISTICS AND MECHANISM OF AI 04
TYPES OF AI 05
APPLICATION IN AGRICULTURE AND PLANT PATHOLOGY 06
STARTUPS IN AGRICULTURE 07
POPULAR APPS USING AI 08
ADVANTAGES AND DISADVANTAGES 09
FUTURE ASPECTS AND CONCLUSION 10

INTRODUCTION
According to UN FAO, population will increase by 2 billion by 2050 but 4% additional land will come
under cultivation by then.
Agriculture plays critical role in providing food supply for growing population of the world.
As per estimates of ICAR the demand for food grains would increase from 192 mt. in 2000 to 345
mt. in 2030.
Agriculture is facing problems starting from land preparation till marketing of produce.
In this one of the major problems is plant protection because crop loss due to disease is 40%.
Disease identification is challenging due to lack of necessary expertise and infrastructure.
To address these problems need of technology is inevitable which may be through use of Artificial
Intelligence.

ARTIFICIAL INTELLIGENCE [AI]
Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed
to think like humans and mimic their actions.
This term may also be applied to any machine that exhibits traits associated with a human mind such
as learning and problem-solving.
AI is a wide-ranging branch of computer science concerned with building smart machines capable of
performing tasks that typically require human intelligence.
Russell and Norvig, 2009

WHY ARTIFICIAL INTELLIGENCE?
IMPORTANCE OF AI AND WHY SHOULD WE LEARN IT?
It can solve real-world problems very easily and with accuracy.
It can create personal virtual assistant, such as Cortana, Google Assistant and Siri, etc.
We can build such Robots which can work in an environment where
survival of humans can be at risk.
AI opens a path for other new technologies, new devices and new
opportunities.

2000-2010
1994-2000
1987-1994
1959
1940-1956
2010-2018
AI DEVELOPMENT
Birth of AI, Alan Turing created the Turing Test, Conference held in Dartmouth
College, US and the term AI was introduced.
Samuels checkers program used machine learning to defeat human players.
Boom in expert machines in industry like RI or Xcon to help sales to avoid errors in
product suggestions.
Robotic cars drive long distance on the highway, IBM’s-Deep blue defeated chess
champion.
Precision Agriculture using GIS, GPS and Introduction of virtual agents with Siri,
Google now.
Cloud Computing, Apps, IoT, Big Data Analytics, Instant messengers, Networking,
Data Mining & Decision Making.

CHARACTERISTICS OF AI
Capable of processing massive amounts
of structured and unstructured data
which can change constantly.
Ability to reason and draw inference
based to the situation. Context driven
awareness of the system.
Ability to learn based on historical
pattern, expert input and feedback loop.
Capable of analyzing and solving
complex problems in special purpose
and general purpose domain.
BIG DATA REASONING
PROBLEM
SOLVING
LEARNING

HOW DOES ARTIFICIAL INTELLIGENCE
WORK ???

TYPES OF AI
1. REACTIVE MACHINE AI It includes machines that operates solely based on the present data, taking
into account only the current situation. Ex- Deep Blue defeated world champion Garry Kasparov in
chess.
– They can perform narrowed range of pre defined tasks.
2. LIMITED MEMORY AI It can make informed and improved decisions by studying the past data from
its memory. Ex- Self driving cars uses the data collected in the recent past to make immediate
decisions.

TYPES OF AI
3. THEORY OF MIND AI The theory of Mind AI will focus mainly on emotional intelligence so that
human believes and thoughts can be better comprehended.
– It is more advanced one and researches are in progress till date.
– It is speculated to play a major role in human psychology.
4. SELF-AWARE AI It includes machines that have their own consciousness and become self-aware.
– This type of AI does not exist yet.
– It is a little far fetched given the present circumstances.
– In the future, achieving a stage of super intelligence might be possible.

AI IN AGRICULTURE
ESTIMATE DATA ON AI
IN AGRICULTURE
22.68 % Compound Annual
Gross Returns (CAGR) of AI in
Agriculture industry during
2017 – 2021.
Around 75 million Agriculture
IoTs by 2030.
4.1 million data points farms
estimated to generate daily in
2050.
AI IN TRENDING
AREAS OF
AGRICULTURE
1. Pest and Disease control
2. Boost crop yield
3. Seasonal forecasting
4. Enhance IoT device data
5. Better crop selection
6. Chatbots for farmers
7. Agriculture Robots.
Source – Cognilytica, 2019

APPLICATIONS IN PLANT PATHOLOGY
Prediction and
forecasting of plant
diseases
Detection of plant
diseases using
Apps
Application of
chemicals in green
houses through
robots
2 3 4
Survey and data
collection of plant
diseases through
UAV
1

SOME APPLICATION OF AI AND STARTUPS
IN AGRICULTURE
1. Blue River
Technology
Specialises in Weed Control.
2. Harvest CROO
Robotics
Specialises in Crop Harvesting.
3.
Hortau Inc.
Specialises in Web-Based Irrigation
Management System.
4. SkySquirrel
Technologies Inc.
Specialises in Drones and Computer Vision
for Crop Analysis.
5.
aWhere
Specialises in Satellites for Weather
Prediction and Crop Sustainability.
6.
Trace Genomics
Specialises in Machine Learning for
Diagnosing Soil Defects.
7.
Farm Shots
Specialises in Satellites for Monitoring Crop
Health and Sustainability.

AI IN AGRICULTURE STARTUPS IN INDIA
1. CropIn 2010 Bangalore
2. AgNext 2016 Mohali
3. Zentronlabs 2012 Bangalore
4. Intello Labs 2016 Bangalore
5. TartanSense 2015 Bangalore
6. Wolkus Technology Solutions 2018 Bangalore
7. Fasal 2018 Bangalore
8. Farmguide 2014 Gurgaon
9. Agricx Lab 2016 Thane
10. Nebulaa’s Matt 2016 Jaipur

ROBOTICS
ARTIFICIAL INTELLIGENCE
DEEP LEARNING
MACHINE LEARNING
ANN
REINFORCEMENT
LEARNING
RNN
CNN
UNSUPERVISED
LEARNING
SUPERVISED LEARNING
COMPUTER VISION
NATURAL LANG. PROCESSING
EXPERT SYSTEM
FUZZY LOGIC
D
O
M
A
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N
O
F
A
I
D
O
M
A
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N
O
F
A
I

MACHINE LEARNING
Machine Learning is the science of getting machines to interpret, process and analyze data in order to
solve real-world problems.
Under Machine Learning there are three categories –
1. Supervised Learning – ‟Train me”
2. Unsupervised Learning – ‟I am self sufficient in learning”
3. Reinforcement Learning – ‟My life My rules! (Hit & Trial)”

1. SUPERVISED LEARNING is a technique in which we teach or train the machine using data which is
well labeled. Labeled data set is the teacher that will train you to understand patterns in the data.
2. UNSUPERVISED LEARNING involves training by using unlabeled data and allowing the model to act
on that information without guidance. Model is not fed with labeled data and it figures out patterns and
the differences between.

EARLY DETECTION OF GRAPES DISEASES USING MACHINE
LEARNING AND IOT
1. This model uses Wireless Sensor Network (WSN), IoT and statistical model i.e. Hidden Markov Model.
2. Take input from temperature, relative humidity, leaf wetness sensor and convert it into digital format.
3. Transfer data to server using Zig-Bee wireless communication protocol and store in database.
4. Classified grapes disease based on the various parameters with the help of huge data gathered over a
period of time.
DISEASE NAME TEMP(°C) RH (%) LH (HRS.)
Bacterial leaf Spot 25- 30 80-90 -
Powdery Mildew 21-27 More than 48 -
Downey Mildew 17- 32.5 More than 48 2-3
Anthracnose 24-26 - 12
Bacterial Canker 25-30 > 80 -
Rust 24 75 -
WEATHER DATA
Patil et al.(2016)

PROPOSED VINEYARD MONITORING SYSTEM
ARCHITECTURE
1. IoT enabled an object which uses web services
and interacts with those objects.
2. IoT collects sensor data, manage cloud
services and easy to get recommendations
about weather forecast data etc. with a fast
speed.
3. It was helpful for early and accurate detection
of diseases and reduce the manual detection
efforts.
4. Farmers received information about a schedule
of fertilizers, pesticides spraying, irrigation etc.
5. Thus, improvement in the quality and quantity
of grapes is achieved along with reduction in
excessive use of pesticides. Patil et al.(2016)

DEEP LEARNING (DL)
Deep Learning is the process of implementing Neural Networks on high dimensional data to gain
insights and form solutions.
– DL is an advanced field of Machine Learning that can be used to solve more advanced problems.
– It’s used in face verification algorithm on Facebook, self-driving cars and also virtual assistants
like Google, Alexa etc.

DL NEURAL NETWORK
Deep Learning consists of Multi-Neural Network Architecture. This mainly includes networks like ANN,
CNN and RNN.
1. ARTIFICIAL NEUTRAL NETWORK (ANN) – stores data in the form of numbers.
2. CONVOLUTIONAL NEURAL NETWORK (CNN) – secures data in the form of image. It is widely
used in Plant Pathology i.e., for image processing.
3. RECURRENT NEURAL NETWORK (RNN) – Data in the form of time and series. Its used in
developing forecast model.

TOMATO LEAF DISEASE DETECTION USING ARTIFICIAL
INTELLIGENCE AND MACHINE LEARNING
1. Focused on collection of the data of diseases on plants and training a model for disease detection.
2. Uses of CNN which helps in recognition, classification and also smart phone based size and colour
detection of leaves on plant for detection of disease.
3. Followed the steps like Image acquisition, pre-processing of the image, Segmentation, feature
extraction, classification and prediction of classified disease.
4. Helps us to recognize whether crop is infected or not, classify diseases with the help of colours
developed due to disease and thus suggesting various remedies for it based on severity of disease.
Septoria Leaf Spot Early Blight Mosaic Virus
Late Blight
Langar et al. (2020)

FLOW CHART OF PROPOSED SYSTEM
Input test
Image
Pre-
processing
Convert to
an Array for
Comparison
Healthy
Defect
Segregated
Database
Pre-
processing
Training
Model
Using
CNN
CNN Based
Classification
Display
Disease
and
Remedy
NO
YES
The steps involved in model are –
– Image Acquisition
– Image Preprocessing
– Image Segmentation
– Feature Extraction
– Classification and leaf disease detection
Future scope is there for mobile apps which is useful for farmers as proper guide
to do agriculture. In future we can do the disease detection techniques using
various parts of the crops or plants like stem, flower, and root.
Langar et al. (2020)

Source : Computers and electronics in
agriculture.2018. 157:63-76.

DETECTION OF GRAPEVINE YELLOWS SYMPTOMS INVitis
vinifera L. WITH ARTIFICIAL INTELLIGENCE
I. Present novel system, utilized CNN for detection of Grapevine Yellow
using color images of leaf clippings.
II. Plants were surveyed, sampled, photographed and then diagnosed for
GY with DNA analysis of the pathogen.
III. ImageNet had 1,50,000 data samples and 1000 different populations
are also used.
IV. With supervised learning, the ML algorithm learns from example
(datasets).
V. The algorithm is then retrained with the data at hand.
Cruz et al. (2018)

An over view of the segmentation procedure –
1. Converting the original RGB image to gray scale with a YIQ
transformation.
2. Blurring the gray scale image with Gaussian filter to remove noise.
3. Obtaining a rough leaf mask by thresholding with Otsu’s algorithm.
4. Convolving the leaf mask with a morphological Ops to remove petiole
from the final image.
5. Finally, median filtering with 11 × 11 filter to remove small objects due
to noise.
RESULTS –
1. System had an accuracy of 98.96%.
2. Deep learning has 35.97% and 9.88% better predictive value without
DL and trained human respectively.
Cruz et al. (2018)

EXPERT SYSTEM
An expert system is an AI-based computer system that learns and reciprocates the decision-making ability
of a human expert.
– Expert systems use if-then logical notations to solve complex problems.
– It does not rely on conventional procedural programming.
– Expert systems are mainly used in information management, medical facilities and Aarogya Setu app.

EXPERT SYSTEM FOR DIAGNOSIS PEST AND DISEASE IN
FRUIT PLANTS
Dewanto and Lukas (2014)
1. Knowledge base regarding pests and diseases of plants obtained from experts and
literatures and internet.
2. These are transferred into a computer using software tools Exsys that combines a
powerful rule editor with a flexible visual interface of decision trees and inference
engine.
3. Knowledge is represented using a decision tree and then be converted into the form of
if-then rules that are stored in the knowledge base.
4. In the inference engine the process occurs to manipulate and directing rules, models
and facts stored in the knowledge base, in order to get solutions or conclusions.
Control techniques used is the backwards chaining i.e. started from the symptoms to
get conclusions about the pests and diseases that occur.

TREE DIAGRAM FOR RULE-PATH
The uniqueness of the expert system is the ability
to reason.
Results showed that the development of this
expert system can be used to assist users in
identifying the type of pests and diseases on fruit
plants

STEPS
DISPLAY OF IF-THEN RULES SYMPTOMS ON LEAF
RESULTS OF DIAGNOSIS AND
SUGGESTIONS

FUZZY LOGIC
Fuzzy logic is a computing approach based on the principles of “degrees of truth” instead of the usual
modern computer logic i.e. boolean in nature.
– Used in the medical fields to solve complex problems that involve decision making.
– Generally incorporated in automatic gearboxes, degree of disease (low to severe).

WEATHER BASED PLANT DISEASES FORECASTING USING
FUZZY LOGIC
1. Collection of sample date for corn rust and
gray leaf spot for forecasting.
2. Weather forecasting using meteorological data
like temp., humidity, LW that plays the vital
roles in the growth of pathogens.
3. Use of Fuzzy Rules to classify disease as very
high, high, medium, low and very low.
Tilva et al. (2013)
DISEASE RUST GRAY SPOT
L.P. Day Temp °C 10-28.5 25-32
H.P. Day Temp °C 18.3-23.8 27-30
H. P. LWS 6 hrs 12 hrs
% RH 98-100 90
L.P. – Low Probility H.P.– High Probility
NO FUZZY RULES TO ESTIMATE PLANT
DISEASE
1 If (Humidity is dry) and (Temp. is very low) and
(LWD is very low) then Disease is very low.
2 If (Humidity is dry) and (Temp. is very low) and
(LWD is low) then Disease is very low.
3
….
If (Humidity is dry) and (Temp. is very low) and
(LWD is medium) the Disease is very low.
50
….
…..
If (Humidity is moderate) and (Temp. is very high)
and (LWD is very high) then Disease is low.
124 If (Humidity is very high) and (Temp. is very high)
and (LWD is very high) then Disease is very high.
125 If (Humidity is very high) and (Temp. is very high)
and (LWD is very high) then Disease is very high.

RESULTS –
1. Knowledge base is generated by literatures
surveys.
2. This expert system utilizes the knowledge of
favourable climate conditions for different
causal organisms of the diseases.
3. Using this, the fuzzy inference system is
developed, that generates the output in
terms of linguistic variables.
4. This linguistic output gives the early warning
to farmers about the probability of
occurrence of the disease. Farmers can take
appropriate action on time.
CONCEPTUAL DIAGRAM OF WEATHER BASED PLANT
DISEASES FORECASTING SYSTEM
The Expert System
for Plant Disease
Forecasting
Disease
probability
output in terms
of Linguistic
variable
Knowledge Base
for Pathogens and
Conductance
Environment
Fuzzy Inference
Engine for Plant
Disease
Forecasting
Temperature
Measurement
Fuzzy Inference
Engine to
estimate Leaf
Wetness Duration
Humidity
Measurement
Knowledge base for
Temp and Humidity
Tilva et al. (2013)

INTERNET OF THINGS (IoT)
IoTs describes the network of physical objects – “things” – that are embedded with sensors, software,
and other technologies for the purpose of connecting and exchanging data with other devices and
systems over the Internet.
– IoT in an agricultural context refers to the use of sensors, cameras and other devices to turn every
element and action involved in farming into data.
– Large data sets on Weather, moisture, plant health, mineral status, chemical applications, pest
presence can be generated.
– Accordingly via software algorithms store and transfer data for development of models.

RICETALK: RICE BLAST DETECTION USING INTERNET OF T
HINGS AND ARTIFICIAL INTELLIGENCE TECHNOLOGIES
1. RiceTalk utilizes non-image IoT devices including the weather
station and the soil sensors for precision monitoring and the
cultivation actuators for irrigation, pest control and fertilization
along with CNN to detect rice blast.
2. AgriTalk sensors generate non-image data that can be
automatically trained and analysed by the AI mechanism in
real time.
3. It also propose an innovative spore germination mechanism as
a new feature extraction model for agriculture.
4. The accuracy of the rice talk prediction on rice blast is about
89.94%.
Chen et al. (2019)
– AgriTalk graphical user interface called (AgriGUI).
– Observation Data Inquire System (CODiS).
IoT part (above), AI part ( below) and
AgriTalk Dashboard

ROBOTICS
Robotics is a branch of Artificial Intelligence which focuses on different branches and application of
robots. It agents acting in a real-world environment to produce results by taking accountable actions.
Ex. Sophia the humanoid is a good example of AI in robotics, Agri-robo, Robotic Sprayers, robotic
strawberry harvester.

e-AGROBOT- A ROBOT FOR EARLY CROP DISEASE
DETECTION USING RASPBERRY PI
1. Camera captures the images of the crop and sends them to Raspberry Pi for
processing by using image processing techniques.
2. Performs various image processing techniques such as image acquisition,
image pre-processing, image segmentation and feature extraction using CNN.
3. Robot helps the farmer to take informed decision locally or allows connecting
with other existing services like upload the pictures for expert opinion.
4. Intimates the farmer using IOT about the crop health condition and disease
affected to the crop and its remedies and indicates the temperature and
humidity of the crop using sensor.
5. Performs pests spraying mechanism to the diseased crops to prevent from
spreading throughout the field.
Kalyani et al. (2020)
Prototype of e-Agrobot
Raspberry Pi

THE OPERATION OF E-AGROBOT FLOWCHART
RESULTS –
Detected Bacterial spot, Yellow leaf curl virus, Late blight in
plants through image processing technique.
FUTURE SCOPE –
1. Moisture sensor identifies the nature of soil and gives the
information about which crops has to be grown on that
particular field for better yielding.
2. Levelling sensor concentrates on which stage of the plant
will be affected, that particular level sensor will be activated
and identify the disease.
3. Navigation sensor helps the particular position and direction
of robot in the field.

NOTIFICATION TO THE FARMER
REMEDIES FOR THE RESULTANT DISEASE
DETECTION OF THE DISEASE

AUTONOMOUS DETECTION OF PLANT DISEASE SYMPTOMS
DIRECTLY FROM AERIAL IMAGERY
1. Visual disease scoring in a large area is time-consuming and
human evaluations are subjective and prone to error.
2. Demonstrated an automated, high-throughput system for the
detection of NLB in maize caused by Septoshaeria turcica in
field images of maize plants.
3. Use of an UAV to acquire high resolution images and training
by CNN model achieved 95.1% accuracy.
4. The CNN model helped to create interpretable heat maps of
the original images, indicating the locations of putative lesions.
5. Detecting lesions at a fine spatial scale allows for the potential
of unprecedented high resolution disease detection for plant
breeding and crop management strategies.
Wu et al.(2019)
Representative image of (NLB)
symptom

Stages and CNN Training
Sample images from unmanned aerial vehicle (UAV)
A. August 2017
B. September 2017
A B
Wu et al.(2019)

POPULAR APPS USING AI
Plant Doctor
Rice Doctor
Crop Doctor
Leaf Doctor
PLANT DOCTOR – A joint venture of Green Savers and the United Nations
Volunteers (UNV).
RICE DOCTOR – International Rice Research Institute (IRRI), Lucid team at the
University of Queensland, Australia, Philippine Rice Research Institute (PhilRice),
Philippines and Research Institute for Rice, Indonesia.
PLANTIX – Progressive Environmental and Agricultural Technologies (PEAT),
Germany in collaboration with ICRISAT, CIMMYT, FAO and PJTSAU.
CROP DOCTOR – IGKV-NIC, Raipur, Chhattisgarh.
LEAF DOCTOR – Cornell University and the University of Hawaii
at Manoa, College of Tropical Agriculture and Human Resources.
RICEXPERT – ICAR-National Rice Research Institute (NRRI), Cuttack.
PESTOZ-Identify Plant Diseases – AgroConnect India Pvt. Ltd., Chhattisgarh.

PLANTIX APP
1. App covers 30 major crops and detects 400+ plant diseases
2. Available in 18 languages and downloaded more than 10 million times.
3. This makes Plantix the #1 agricultural app for damage detection, pest and disease control and yield
improvement for farmers worldwide.

ADVANTAGES OF AI DISADVANTAGES OF AI
1. Reduction in human error and human dependence.
2. Takes risks instead of humans.
3. Available 24×7.
4. Helping in repetitive Jobs.
5. Digital assistance.
6. Faster decisions.
7. Multi tasking ability.
8. Handling multi-variety and multi-dimensional data.
1. Unemployment.
2. High costs of creation.
3. No emotions.
4. Makes people lazy.
5. Lacking out of box thinking.
6. Limitation in accessibility.
7. No improvement with experience.

APPLICATION OF ARTIFICIAL INTELLIGENCE TO TRACK PLANT DISEASES

APPLICATION OF ARTIFICIAL INTELLIGENCE TO TRACK PLANT DISEASES

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