ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN AGRICULTURE Agriculture is the backbone of India's economy. It is the principal livelihood for over 58% of the rural households. But it faces difficult challenges from sowing to harvest. Hence modernisation of agriculture is most needed to address these challenges. In agriculture there is a quick adaptation to AI in its various farming techniques where Artificial Intelligence (AI) is one of the key areas of research in computer science with its rapid technological advancement and vast area of application, AI is becoming relevant very rapidly because of its robust applicability in the problems particularly that cannot be solved well by humans. Such an area of extreme importance is agriculture where about 80% of the population is directly engaged on 159.7 million hectares of agricultural land. Such a venture cannot run smoothly. Hence farming solutions which are AI powered enable a farmer to do more with less, enhancing the quality, also providing a quick GTM (go-to-market strategy) strategy for crops. A direct application of AI (Artificial Intelligence) or machine intelligence across the farming sector could act to be an apotheosis of shifting of traditional farming practice today. AI powered agriculture, analysing its service in interpreting, acquiring and reacting to different situation to enhance efficiency. Artificial intelligence technology is supporting different sectors in agriculture to boost productivity and efficiency. AI solutions are assisting to overcome the traditional challenges in every field. Intervening of AI in agriculture is helping farmers to improve their farming efficiency and reduce environmental hostile impacts. The agriculture industry strongly and openly grasped AI into their practice to change the overall outcome. AI is shifting the way of food production where the agricultural sector's emissions have decreased by 20%. Inculcating AI technology in agriculture is helping to control and manage any uninvited natural condition. Agriculture is the backbone of India's economy. It is the principal livelihood for over 58% of the rural households. But it faces difficult challenges from sowing to harvest. Hence modernisation of agriculture is most needed to address these challenges. In agriculture there is a quick adaptation to AI in its various farming techniques where Artificial Intelligence (AI) is one of the key areas of research in computer science with its rapid technological advancement and vast area of application, AI is becoming relevant very rapidly because of its robust applicability in the problems particularly that cannot be solved well by humans. Such an area of extreme importance is agriculture where about 80% of the population is directly engaged on 159.7 million hectares of agricul

1. Artificial Intelligence and Machine Learning in
Agriculture
COURSE NAME: CURRENT TRENDS IN AGRONOMY
BY- Kanika Bhakuni
49873
Ph.D 1st Year
Department of Agronomy

2. FLOW OF THE PRESENTATION
• Introduction to Artificial intelligence (AI)
• Evolution of AI
• Difference between AI and Human intelligence
• Application of artificial intelligence
• AI in agriculture
• Benefits of AI in agriculture
• Kinds of Artificial intelligence
• Application of AI in agriculture
• AI startups
• Challenges in adoption of AI
• Necessary steps and policies for implementation
AI
in
Agriculture

3. ARTIFICIAL INTELLIGENCE
Artificial ="man-made," and,
Intelligence ="thinking power",
hence AI means "a man-made thinking power.”
Definition
Artificial intelligence (AI), branch of computer science, enables machines to
learn, make decisions and perform tasks without human intervention.
• The basic concept of AI
– To develop a technology which functions like a human brain (thinking,
learning, decision making, solving problems etc.)
– On this ground, intelligent software and systems are developed.
– These software's are fed with training data and further these intelligent
devices provide us with desired output for every valid input.
(Parekh et al., 2020; Jani et al., 2019).
AI
in
Agriculture

4. EVOLUTION OFAI
•Word AI was proposed by John
McCarthy, an American
computer scientist in first
Artificial Intelligence conference,
Dartmouth Conference(1956).
•He is called as the “Father of
Artificial Intelligence” .
AI
in
Agriculture

5. Human
Intelligence
Artificial
Intelligence
• Intuition, Common sense,
Judgments, Creativity, Beliefs
etc.
•The ability to demonstrate
their intelligence by
communicating effectively.
•Plausible Reasoning and
Critical thinking
•Ability to simulate human
behavior and cognitive
processes.
•Capture and preserve
human expertise.
Fast response. The ability to
comprehend large amounts
of data quickly.
AI
in
Agriculture How AI is different from human brain?

6. APPLICATION OF ARTIFICIAL
INTELLIGENCE
AI
in
Agriculture

7. TYPES OF AI
Based on capabilities
1. Weak AI or Narrow AI:
• Narrow AI is a type of AI which is able to perform a dedicated task with intelligence.
• The most common and currently available AI is Narrow AI in the world of Artificial Intelligence.
2. General AI:
• General AI is a type of intelligence which could perform any intellectual task with efficiency
like a human.
3. Super AI:
• Super AI is a level of Intelligence of Systems at which machines could surpass human
intelligence, and can perform any task better than human with cognitive properties. It is an outcome
of general AI.
AI
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Agriculture

8. MAJOR SUB-FIELDS OF AI
AI
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Agriculture

9. 1. Machine Learning
• ML is defined as the scientific field that gives machines the ability to
learn without being strictly programmed.
• Objective : learning from “experiences” to perform task, which is now
trained.
• After learning process, ML models (trained) used for testing data and
classify, cluster and predict data.
TRAINING DATA
(LABELED/UNLABELED)
NEW EXAMPLES
PREDICTIVE OUTPUT
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Agriculture

10. CONTINUED..
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Agriculture

11. Learning Models
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Agriculture

12. • Supervised learning involves learning a function that maps an input to
an output based on example input-output pairs.
•Decision trees are a popular model, used in operations research, strategic
planning, and machine learning.
CONTINUED..
AI
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Agriculture

13. •Artificial neural networks: ANNs are inspired by the human brain
functionality, emulating complex functions such as pattern generation, cognition,
learning, and decision making
•Support vector machine: The goal of the SVM algorithm is to create the best
line or decision boundary that can segregate n-dimensional space into classes so
that we can easily put the new data point in the correct category in the future.
CONTINUED..
AI
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Agriculture

14. 2. Neural network: The neural
networks are the brain of
artificial intelligence. They are
the computer systems which are
the replica of the neural
connections in the human brain.
3. Deep learning: It is the process
of learning by processing and
analyzing the input data by
several methods until the
machine discovers the single
desirable output. It is also
known as the self-learning of
the machines.
AI
in
Agriculture OTHER TYPES OF SUB-FIELDS

15. 4. Cognitive Computing: While working
on various kinds of tasks with humans, the
machines learn and understand human
behavior, sentiments in various
distinctive conditions and recreate the
thinking process of humans in a computer
model.
5. Natural Language Processing: With this
feature of artificial intelligence, computers
can interpret, identify, locate, and
process human language and speech.
6. Computer Vision: it facilitates the
computer to automatically recognize,
analyze, and interpret the visual data
from the real world images and visuals by
capturing and intercepting them.
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Agriculture

16. • Pest infestation
• Lack of proper irrigation management
• Improper storage management
1. Less productivity
• Change/diverse consumption habits
• Scarcity of natural resources (land, water)
• Youth reluctant to do farming
2. Increasing Population
• Indiscriminate use of fertilizers and pesticides
3. Ill effect to the environment
• Less profitability
• No rural transformation.
4. Less income per capita
Why AI is needed in Agriculture?
AI
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Agriculture

17. Benefits of Artificial Intelligence in Agriculture
Sector
Intelligent
spraying
Agriculture
robots
Enhance
awareness
among farmers
Crop and
soil
monitoring
Increase farm
productivity
Price forecast
Disease
and insect
diagnosis
Helps
investment
planning
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Agriculture

18. AI applications in Agriculture
1. Growth driven by IoT
•Huge data, on weather pattern, soil
parameters, rainfall, pest infestation,
images and researches, is generated.
• Data can be sensed and strong insights
be provided to improve yield with the
help of cognitive IoT solutions.
•For example: Remote sensing and
proximity sensing needs sensors to be
build into satellite and in contact to soil
for taking data related to soil testing.
AI
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19. 2. Image Based Insight Generation
•Computer vision technology, IOT and
drone data can be combined to ensure rapid
actions by farmers.
• Feeds from drone image data can
generate alerts in real time to accelerate
precision farming.
Image
Image
cleaning
Feature
extraction
Map features
with
knowledge
Train
model
Basic model of image based insight generation
•Crop readiness Identification
•Disease detection
•Field management
• Optimizing agronomic inputs
USES
AI
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Agriculture

20. 3. Automated Irrigation System
• Irrigation System based on Artificial
Intelligence and Internet of Things, which can
autonomously irrigate fields using soil
moisture data, Weather patterns and types
of crops to be cultivated.
• AI enabled smart water meters and sensors,
support the saving in water consumption and
budgeting, moisture and temperature sensors
directly interact with field components and
distribute the required water among the crops.
• Smart sensors in sprinklers and drips
irrigation may enable water supply as per
plant need. Additionally, the technology helps
automate greenhouses.
Design of moisture content decision system
AI
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Agriculture

21. 4. Decrease pesticide usage
• Robotics, machine learning, and
computer vision can be used to
manage weeds effectively. Data
collection and analysis by AI
assist farmers in spraying
herbicides to weed-infested fields
and in monitoring weed
infestation.
• Spraying the right amount of
herbicides over an infested area
minimizes the overuse of
pesticides.
START INPUT IMAGE
IMAGE
SPECIFICATION
BACKGROUND
COLOR
REMOVAL
IMAGE GRAY
TRASITION
IMAGE
FILTERING
THRESHOLD
WEED
DETECTION
DISPLAY
RESULT
STOP
Flowchart for weed detection system
Image cleaning
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22. 5.Agriculture Robots(Agbots)
• Agbots with computer vision
algorithms, identify weeds and spray
herbicides precisely. The Smart See
and Spray Model assists in
distinguishing economic plants from
weeds.
• Additionally, robots for picking and
packing robots are being developed by
researchers.
• The other robots available for weed
control are BoniRob, Roboter and
Hortibot.
• Energid Citrus Picking System,
designed exclusively for picking citrus
fruits.
AI
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Agriculture

23. 6.Supply Chain Management
• AI use in supply chain planning,
resource optimization, including
forecasting demand, and logistics will
result in cost savings for farmers.
•Farmers and institutional purchasers
are connected through blockchain
technology which provides direct
communication between two parties
initiate transactions.
•Blockchain technology quickly track
and process information related to
food items right from their source to
the end consumer.
AI
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Agriculture

24. 7. Precision Farming
• Carrying out farm activity
considering spatial and temporal
variability within the farm.
• This is a more accurate and
controlled technique that
replaces the repetitive and labor-
intensive part of farming.
• Concept
– Right input
– Right time
– Right amount
– Right place
– Right manner
• Tools
– GPS (Global positioning
system)
– GIS (geographical information
system
– Remote sensing
– UAVs ( unmanned aerial
vehicles)
• Applications
– Yield monitoring and
mapping
– Variable rate technology
– Micro irrigation
– Weed management using
AI
AI
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Agriculture

25. AI
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Agriculture
CYCLE OF PRECISION AGRICULTURE

26. 8.Unmanned aerial vehicles
(UAVs)
• An agricultural drone is an
unmanned aerial vehicle
used to help optimize
agricultural operations,
increase production and
monitor crop growth.
• Sensors and digital imaging
capabilities can give farmers
a richer picture of their field
• Sensors used
– Visual
• Aerial mapping
• Imaging
• Surveying
– Thermal
• Heat detection
• Livestock detection
– Multispectral
• Plant heath
• Water quality
• Vegetative index
– Hyper spectral
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27. How are drones being used in agriculture?
• Monitoring plant health
• Monitoring field condition
• Planting and seeding
• Spray application
• Security
• Drone pollination
• Drone irrigation

28. AI
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29. 9.Robotics in Agriculture
1. Chat bots
• The Farmer chatbot can be
modified to meet sector-specific
requirements.
• Chatbots include automated
interactions with conversational
virtual assistants.
• This emerging technology can
provide solutions to farmers'
queries and advice on specific
farm challenges.
2. Driverless Tractors
• All farm operations are performed
autonomously and precisely.
• Sensors mounted on tractors
regulate and perform all required
practices, monitor obstructions,
and apply required farm inputs.
AI
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Agriculture

30. AI in crop management
1. Yield prediction
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31. 2. Disease detection
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32. 3. Weed management
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33. AI
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34. AI Startups
1. Blue River Technology: A robot named, See & Spray was developed
by a California-based company Start-up that uses computer vision to
monitor and spray herbicide precisely on weeds in cotton plants.
AI
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Agriculture

35. 2. Harvest CROO robotics: Harvest CROO Robotics has created a robot
for strawberry harvesting that can harvest 8 acres and replace 30
human laborers in a single day.
3. PEAT (machine vision for pests/ soil defects diagnosis): To identify
soil potential defects and nutritional deficiencies, Plantix, has been
developed by agricultural tech startup PEAT.
4. DeHAAT :Provider of end-to-end farming services to the farming
communities. The company claims to offer services for farmers
including crop consultation, crop reminder, local voice calls, market
to sell and buy inputs and produce, advisory services, etc.
AI
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Agriculture

36. 5. CropIn: It is efficient in mapping, monitoring, and managing farming decisions
precisely. Digitization of farms with CropIn is bringing automation to
agriculture while reducing resource utilization.
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Agriculture

37. 6. FASAL: Provider of AI and IoT platform for precision agriculture. It offers a
cloud-based platform that collects the microclimatic data captured by the
on-field sensors.
AI
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Agriculture
7. Agribolo: It empowers
farmers with latest
mandi/weather updates, best
farm practices and expert
advice. It also provide “Agri
Mart” & “Agro Services” a
marketplace to buy/rent/sell
agri-based products &
services along with e-mandi
services

38. Challenges in adoption of AI
• Lack of infrastructure.
• Lack of awareness and knowledge.
• Deep rooted faith for conventional type of
agriculture.
• Exposure of farming to external factors.
• AI based machine needs lot of training for
precise prediction.
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39. Necessary steps and policies for
implementation
• Awareness among small and medium
business agriculture holders.
• More research in AI in relation to plant
science.
• Government should mandate laws
regarding use of AI.
• More investment in the technology by
government.
• Provision of subsidy to users.
AI
in
Agriculture