Agriculture is the mainstay of Indian economy as about 60% of our population depends directly or indirectly on agriculture.Exploration of technology in digital world gave birth to a whole new field of making intelligent machines i.e. Artificial intelligence (AI). AI is making a huge impact in all domains of the industry. Every industry looking to automate certain jobs through the use of intelligent machinery. Factors such as climate change, population growth and food security concerns have propelled the industry into seeking more innovative approaches to protecting and improving crop yield. As a result, AI is steadily emerging as part of the Agricultural industry’s technological evolution. The automation in agriculture is the main concern and the emerging subject across the world. AI in agriculture not only helping farmers to automate their farming but also shifts to precise cultivation for higher crop yield and better quality while using fewer resources.Technological advancement in the future will provide more useful applications to the sector helping the world deal with various farming challenges used to be faced in traditional agricultural practices.

2. DOCTORAL SEMINAR
DEPARTMENT OF SOIL SCIENCE AND AGRL. CHEMISTRY
INSTITUTE OF AGRICULTURAL SCIENCES
BANARAS HINDU UNIVERSITY
VARANASI - 221005
Advisor
Prof. A. P. Singh
Speaker
Aditi Chourasia
I.D. No. PS-17055
BANARAS HINDU UNIVERSITY, VARANASI
Artificial Intelligence (AI) : Basics and
application in Agriculture

3. Contents…
 Introduction
 Evolution of AI
 Types of AI
 AI in context of humans
 Domains of AI
 Need, scope and application of AI in
Agriculture
 Internet of Things in Agriculture
 Conclusion

4. Artificial intelligence (AI) is the simulation of human intelligence processes by
machines, especially computer systems.
Artificial intelligence
These processes include :
learning (the acquisition of information and rules for using the information)
reasoning (using rules to reach approximate or definite conclusions)
Problem Solving
Perception
Intelligence : The ability to acquire and apply knowledge and skills.
With the advent of technology in this digital world, we humans have pushed
our limit of the thinking process and are trying to coalesce normal brain with an
artificial one. This continuing exploration gave birth to a whole new field
Artificial intelligence.

5. 1943
Evolution of
Artificial Neuron
Alan Turing- Turing
test to speculate the
possibilty of cretaing
machines that can
think
1950 1951
Game AI
1956
The Birth of AI :
John McCarthy first
coined the term
“Artificial Intelligence”
at the Darthmouth
Conference
1961
The First AI
Chatbot called
ELIZA was
introduces
First Intelligence
robot WABOT-1
1972 1974-80
First winter
AI
1980
Expert system
1987-1993
Second
winter AI
1997
IBM Deep Blue beats
world champion Garry
Kasparov in the game of
Chess
2002 2011 2012 2015
AI in Home:
Roomba
IBM s Watson wins a
quiz show
Google Now
Amazon Echo
Evolution of Artificial Intelligence

6. Types of Artificial intelligence
Artificial Narrow
intelligence
Artificial General
intelligence
Artificial Super
intelligence

7. Artificial Narrow intelligence (ANI)
ANI also known as weak AI involves applying AI only to specific tasks.
Examples :
Alexa :It operates within a limited predefined
range of functions
Face verification at Apple iPhone
Autopilot feature at Tesla
The Social Humanoid,
Sophia, built at
Hanson Robotics
Finding the optimal
path through Google
Maps

8. Artificial General intelligence(AGI) :
 AGI also known as strong AI, involves machines
that possess the ability to perform any
intellactual task that a human being can.

9. Artificial Super intelligence (ASI) :
 ASI is a term referring to the time when the capability of
computers will surpass humans.
 ASI is presently seen as a hypothetical situation as depicted in
movies and science fiction books where machines will take over
the world.

10. Understanding AI in context of
Humans

11. Computer Science
Artificial intelligence
Speech
recognition
Statistical
Learning
Natural
language
processing
(NLP)
Symbolic
Learning
Computer
Vision
Robotics
Deep
Learning
Machine
learning
CNN RNN
Computer
Vision
Object
Recognition
image
processing
Communicate
NLP
Image
Processing
Networks
of
Neuron
Pattern recognition
Neural networks

12. Domains of
Artificial Intelligence…

13. Domains of
Artificial
Intelligence
Machine Learning
Deep Learning
Robotics
Natural Language
Processing
Fuzzy Logic
Expert System

14. Artificial
Intelligence
The science of getting
machines to mimic the
behaviour of humans.
Machine
Learning
A subset of AI
that focuse on
getting
machines to
make
decisions by
feeding them
data.
fx
Deep
Learning
A subset of Machine
Learning that uses the
concept of neural
networks to solve
complex problems.

15.  Implementation of AI involves learning process of machines. This
brings us to a sub-domain in this AI field“ Machine learning”.
 The sole purpose of machine learning is to feed the machine with data
from past experiences and statistical data so that it can perform its
assigned task to solve a particular problem.
 It is because of machine learning that the domain of big data and data
science has evolved to such a great extent.
 Machine learning is a mathematical approach to build intelligent
machines.
 Examples:
 Google Maps
 Product recommendation on online shopping platform
 Self Driving cars
Machine learning :

16. Deep Learning
A subset of Machine Learning that uses the concept of neural networks
to solve complex problems.
Artificial Neural Network
An artificial neural network (ANN) is the piece of a computing system
designed to simulate the way the human brain analyzes and processes
information.
Neural Networks forms the base of the Deep Learning.

17. Fuzzy Logic
Fuzzy Logic (FL) is a method of reasoning that resembles human
reasoning.
This approach is similar to how human performs decision making.
Involves all intermediate possibilities between YES and NO.
FL works on the levels of
possibilities of input to achieve a
definite output.

18. Natural Language Processing (NLP)
Any natural way of communication is Natural Language, like speech, text, notes
etc.
Machine language - machine talks to other machines (convetred into bits and
bytes)
NLP is one of the technique to understand and interpret the natural language by a
machine.
Applications :
 Sentimental analysis
 Chatbots
 Speech recognition
 Voice assistant like siri, google assistant, cortana
 Machine translation like google translator
 Spell checking
 Keyword search

19. Expert Systems
These are computer application or a piece of softwares which uses database
of expert knowledge to offer advice or make decisions.
Characteristics :
1. High performance
2. Reliable
3. Highly responsive
4. Understandable
Architecture

21. Need of AI in Agriculture
According to UN Food and Agriculture Organization, the
population will increase to 10 billion by 2050.
About 70% increase in food production will be required to
meet food demands.
Only 4% additional land will be there by 2050.
Farm enterprises require new and innovative technologies to
face and overcome these challenges.
By using AI these challenges can be resolved upto a great
extent.

22. Scope of AI in Agriculture
 Growth driven by cognitive IOT
 Images from Drones
 Proximity sensing and remote
sensing
 Soil testing
 Image based insight generation
 Disease detection
 Crop readiness identification
 Field managemnet
 Identification of optimal mix for
agronomic products
 Cognitive solutions
Soil condition
Weather forecast
Type of seeds
Infestation in a certain area
 Health monitoring of crops
 Hyper spectral Imaging
 3D laser scanning
 Automation techniques in
irrigation and enabling farmers
 Machine learning
 Automate irrigation
 Increase overall yield

23. Internet of Things [IoT] In Agriculture
The Internet of things (IoT) 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.

24. There are numerous IoT applications in farming. Such as collecting data on
temperature, rainfall, humidity, wind speed, pest infestation, and soil
content.
This data can be used to:
 automate farming techniques,
 take informed decisions to improve quality and quantity,
 minimise risk and waste,
 reduce effort required to manage crops.

25. UAVs and Drones
Unmanned Aerial Vehicles commonly known as a drone are:
An aircraft without a human pilot on board
component of an unmanned aircraft system (UAS) ; which
include
 a UAV,
 a ground-based controller, and
 a system of communications between the two
 The flight of UAVs may operate with various degrees
of autonomy: either under remote control by a human
operator or autonomously by onboard computers referred to
as an Autopilot.

26. Sensors
 Sensors are devices that detect external information, replacing it
with a signal that humans and machines can distinguish.
 Play an important role in creating solutions to IoT.
 There's a wide range of sensors used in smart agriculture including
 Soil sensors,
 humidity sensors,
 moisture sensors,
 Light sensors,
 air sensors
 temperature sensors
 CO2 sensors,
 solar energy sensor and many others
Soil moisture
sensor

27. Software
 Software is a collection of instructions and data that tell the
computer how to work
 It is the programs and other operating information used by a
computer.
 IoT softwares and master applications are responsible for
 data collection,
 device integration,
 real-time analytics, and
 application and process extension within the IoT network.

28. IoT Challenges in Agriculture
 Connectivity
 Design and durability
 Limited resources and time
Solutions provided by IoTs to overcome such problems
 Precison Farming
 Smart Irrigation
 Smart Greenhouse

29. ARTIFICIAL
INTELLIGENCE
Realtime weather
forecasting
Weed
Management
Disease
Management
Glaucus CBR system
for Fisheries
Drones for crop
spraying
Face recognition
systems for
domestic cattles
Fruit Harvesting General Crop
Management
Pest Management
Agricultural Product
Monitoring And
Storage Control
AI based
Green House
Soil And Irrigation
Management
Application of AI in Agriculture

30. Automated Irrigation Systems
 It refers to the operation of the system with no or just a minimum of
manual intervention beside the surveillance.
 Almost every system (drip, sprinkler, surface) can be automated with
help of timers, sensors or computers or mechanical appliances.

31. Advantages :
 Eliminates the manual operation of opening or closing valves
 Possibility to change frequency of irrigation and fertigation
processes and to optimise these processes
 Adoption of advanced crop systems and new technologies,
especially new crop systems that are complex and difficult to operate
manually
 Use of water from different sources and increased efficiency in water
and fertiliser use
 System can be operated at night, water loss from evaporation is thus
minimised
 Irrigation process starts and stops exactly when required, thus
optimising energy requirements

32. Real-time weather forecasting
 Cheaper sensors and better connectivity expand the accessibility of the
internet of things (IOT),
 Car, truck, solar panel, connected traffic light, cellphone, smart household air
conditioning systems, etc. could be used as a source of real-time information
for improving forecasting.
Panasonic makes TAMDAR, a speciality weather sensor installed on commercial
airplanes.
In 2013 Monsanto bought Climate Corporation .
Among the services Climate Corporation provides, one if its main focuses is
hyper-local weather forecast information for farmers.
It uses a variety of sources and machine learning to optimize weather predictions
specifically for agriculture.

33. Weed Management
 Modern AI methods are being applied to minimize the herbicide
application through proper and precise weed management
 A smart sprayer locate weed spots in real time and manage to spray the
desired chemical only on the proper location.
 Various sensors and techniques used for weed detection are machine
vision (image segmentation), Spetral analysis, remote sensing and
thermal images.
 Blue River Technology, Sunnyvale, CA, USA is a great example.
 In recent researches AI technologies are used to make smart sprayers.
Deep learning neural network analyzes much more complex properties
than an image segmentation alone
Hortibot Robot for Weeding
Smart spraying : precision herbicide application

34. Disease Management
Computer aided systems are being used worldwide to diagnose the
diseases and to suggest control measures.
Real-time diagnosis is enabled using the latest Artificial Intelligence (AI)
algorithms for Cloud-based image processing.
the AI model (CNN) was trained with large disease datasets, created
with plant images collected
satellite and drone imagery, and sensors on the cameras can detect
small parts of a field that are inflicted by disease, and a farmer acts on
that information and treats that tiny area with a targeted application.
Identification of diseases and getting solutions with a mobile app by
photographing affected plant parts
Plantix, powered by the Strey’s Berlin-
based startup PEAT GmbH, now uses
machine-learning and scientific image
data supplied by ICRISAT and local
research institutions to bring 75,000
daily users information about pests and
diseases.

35. Pest Management
computerized systems are developing since decades that could identify the
active pests and suggest control measures.
Different sensors are used to monitor the growth of
pests and take further countermeasures to manage
them.
1. Low-power Cameras and Sensors
2. High-power Thermal Sensors
3. Fluorescence Image Sensing
4. Acoustic Sensors
5. Gas Sensors
Advantages offered by IoT :
Monitoring Pest Infestation and Crop
Health
Weather monitoring and Analytics
Automated crop health monitoring

36. Fruit Harvesting
Automated Fruit Harvesting Robot by using deep learning
 The automatic harvesting of fruits by a robot involves two big tasks:
(1) fruit detection and localization on trees using computer vision
with a sensor
(2) robot arm motion to the position of the detected fruit and fruit
harvesting by the end effector without damaging target fruit and
its tree.
 A color camera and a Single Shot
MultiBox Detector (SSD) is used to
detect the two-dimensional (2D)
position of the fruit.
[The SSD is one of the general
object detection methods that use
Convolution Neural Network (CNN) ]

37. Agricultural Product Monitoring And Storage Control
TADD (Trainable Anomaly Detection and Diagnosis ) Potato Sorting
Systems.
• A robotic system that sorts and can detect diseases of potatoes
Advantages:
 Higher precision of detection of
affected/unhealthy potatoes
versuse manual selection in
industrial scale
 Lower labour input
 Possibility for higher food safety
assuarance

38. AI in Dairy Farming
 Cattle facial recognition, which is also called the Aadhar of cattle in
India, is the perfect solution for cattle identity problem.
 Mooofarm an AgriTech start-up is working to produce the technology at
scale and work with the government to build a robust cattle identity
mechanism.
 The machine learning algorithm is fed with 20-30 pictures of
each cow taken from different angles, different backgrounds, and
different lighting. (The pictures of two cows may look the same to us,
but they have distinguishing physical features all across their face,
including muzzle and eyes, which the ML catches).
 This technology can become the foundation of multiple auxiliary
services like cattle insurance, cattle loans and government subsidies.
Digital identity

39. Health monitoring
 Cattle health is the most important aspect of any dairy business.
 Mooofarm working with Microsoft to create a product that uses machine
vision to detect whether the cattle has subclinical mastitis using the
images of its udder.
VEEPRO: the Information Center for Dutch breeding cattle.
This AI System is able to prescribe feed rations, medications, health and
welfare conditions for livestock.
A diary cow wears a pedometer to measure its activity.

40. APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN SOIL SCIENCES
 Soil testing and monitoring
 Monitoring of Soil/Land cover/Land management
 Identification of nutrient deficiencies
 Soil fertlization Assesment of soil quality

41. Automated soil testing device is an electronic device which can be used to measure
moisture, humidity, temperature values to ensure the fertility of soil in the field of
agriculture to select the suitable crop and also the type of fertilizer to be used .
 Soil testing and monitoring

42. ionic particles
present in soil sensor
sensed
signal
conditioning
circuit.
processed by
remote location or
designated authority in the
agriculture department
suggestions
further analysis
microcontroller
compare the pre-
stored value with
the actual values
LCD
wireless
trans-receiver measured values are
displayed
Schematic representation of working of Automated soil testing device

43. Remote sensing: Crop Health Monitoring
Hyperspectral imaging and 3D Laser scanning, are capable of rapidly providing
enhanced information and plant metrics across thousand of acres with the spatial
resolution to delineate individual plots and /or plants and the temporal advantage of
tracking changes throughout the growing cycle.
 Remote sensing can aid in identifying crops affected by conditions that
are too dry or wet, affected by insect, weed or fungal infestations
or weather related damage.
 Images can be obtained throughout the growing season to not only
detect problems, but also to monitor the success of the treatment.

44. CASE STUDY :
 Developed by a collaboration of Microsoft, Indian Meteorological
Department (IMD), Acharya NG Ranga Agricultural University
(ANGRAU), and ICRISAT.
 ISAT provides concise farm advisories to farmers on their phones.
These messages are generated after analysis of local and global
historical climate data, current and forecasted weather conditions, crop
systems and soil-related information.
 The tool employs a decision-tree approach to generate SMSes, which
are then relayed to farmers registered for the service.
The Intelligent Agricultural Systems Advisory Tool (ISAT):

45. has helped farmers achieve optimal harvests by advising (via SMS in
local languages) on the best time to sow crops. Farmers in Andhra
Pradesh obtained 30% higher yield with timely advisories from the
Sowing App.
The Sowing App
Farmers get critical information on symptoms, triggers, chemicals as
well as biological treatments of crop diseases on time, preventing
greater damage and loss of crop and income.
The Plantix App:
NADiRA is expected to help increase availability of credit, reduce
exposure to climate risks, and improve smallholders’ productivity.
NADiRA:

46.  Expensive
 Higher maintenance
 Unemployment.
 The robots can change the culture / the emotional
appeal of agriculture.
 Energy cost and maintenance.
 The high cost of research and development.
 Lack of access to poor farmers.
Disadvantages of Automated Farming

47. CONCLUSIONS
AI can be appropriate and efficacious in agriculture sector as it
optimises the resource use and efficiency.
It solves the scarcity of resources and labour to a large extent.
Adoption of AI in agriculture is quite useful.
AI can be technological revolution and boom in agriculture to feed the
increasing human population of world.
It will complement and challenge to make right decision by farmers.

48. References :
• Das S, Ghosh I, Banerjee G & Sarka U.2018. Artificial
Intelligence in Agriculture: A Literature Survey.
• Jha K, Doshi A, Patel P and Shah M. 2019. A
comprehensive review on automation in agriculture
using artificial intelligence. Artificial Intelligence in
Agriculture. Volume 2. Pages 1-12.ISSN 2589-7217.
• edureka an online learning plateform
• Wikipedia

49. THANKYOU