This document provides an overview of artificial intelligence (AI) and its applications in agriculture. It discusses how AI is used in agriculture for automated farming activities, pest and disease identification, crop quality management, and environmental monitoring. The document also covers perspectives on AI progression, from narrow to general to super AI. It discusses recent AI developments in India and applications in agriculture like precision farming, yield prediction, and optimized resource use. Limitations of AI include data and infrastructure challenges. The document concludes that AI can boost agriculture through optimized resource use and complement farmer decision making.

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Contents
1. Introduction to Artificial Intelligence
2. How AI used in Agriculture
3. Perspectives of AI
4. Progress of artificial intelligence
5. Perceptual organization about Artificial intelligence
6. Recent development : Indian Scenario
7. Application of Artificial intelligence in Agriculture
8. Ways they transform the future
9. Artificial intelligence system
10.Limitations
11.Recommendations
12.Conclusion
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3. • AI is the intelligence exhibited by machines, rather than
human or other animals. The intelligent agents which
percieves it’s environment and takes action to maximise
the success (Russel et al., 2003).
• Invented by Herbert Simon (1965). Word coined by John
McCarthy.
• Is not ‘Man Vs Machine’ but is ‘Man and Machine’ synergy.
• AI will benefit most to Medicine sector. …
Artificial Intelligence
3

4. …
• Dealing with the simulation of intelligent
behavior in computers.
• Capability of a machine to imitate intelligent
human behavior.
• Perform tasks that normally require human
intelligence, such as visual perception,
speech recognition, decision-making, and
translation between languages.
04

5. How AI used in Agriculture
Automated farming activities
Identification of pest and disease outbreak before occurrence
Managing crop quality
Monitoring biotic. Abiotic factors and stress
Machine vision systems and phenotype lead to adjustments
05

6. • IA (Intelligence Augmentation).
• Moore’s Law
• Deep learning
• Cloud computing
• Machine learning
• Cartoon conspiracy theory
• Week and strong AI
• Chatbot
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06
Perspectives of AI

7. Progression of artificial intelligence
Artificial
general
intelligence
Artificial
super
intelligence
Augmented
intelligence
Autonomous
intelligence
Narrow AI
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8. • “Losing-it-for-not-using-it” in future.
• “Run away with technology and deal with the
consequences later”.
• AI ‘builds’ AI, inturn ‘AI’.
• Trap of dissipative dependency”
• Shrink the hippocampus creating an
increased risk of dementia.
• Its time to throw the passport in Murray.
• “Sofia’ wants a baby”.
8
Perceptual organisation about AI
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9. • Karnataka government signed MoU with Microsoft
India develop Multi-variant Agricultural Commodity
price forecasting Model.
• Microsoft with ICRISAT deployed a Sowing Advisory
Service in Kharif season under ‘Bhuchetna Project’.
• Microsoft developed Price forecasting Model in
Karnataka.
• IBM providing tools to entrepreneurs/ startups to
develop solutions.
…
Recent developments in India
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10. …
• AI-Powered data driven supply chain
optimization platform by Matrix Partners India
in New Delhi.
• PEAT, Earth Food and V Drone Agro use AI to
assess soil conditions over the cloud.
• SatSure in India, assess imageries of farms and
predict monetary prospects of their future yield.
• Monsanto had trained the AI algorithms for 15
years which could predict the corn variety’s
highest performance in the first year trial.
• Syngenta announced ‘AI for Good’ to provide
seed genetic information as well as climate, soil
data for suitability of the variety to an area.
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11. 11
11

12. 12
Seeing
Operation of system
with no or just a
minimum of manual
interventions beside
the surveillance.
12
Automated Irrigation System

13. 13
TADD
Trainable Anomaly
Detection and
Diagnosis system - is
able to "detect,
identify and quantify
many of the
common blemishes
affecting potatoes"
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14. 14
Drones for Spraying
Precision Agriculture
Mapping/ Surveying
Crop dusting and
spraying
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15. 15
Remote Sensing Based Crop Health Monitoring
• Hyper-spectral
imaging and 3-D
laser scanning
• Multi-censor
collection
approaches of
phenotype data
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16. 16
Face recognition system for domestic cattle
Facial recognition of
cows in dairy units can
individually monitor all
aspects of behaviour
in a group, as well as
body condition score
and feeding.
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17. 17
Veepro: information center for dairy cattle
• Prescribe feed rations,
medications, health and
welfare conditions for
livestock. Recommend
mating partners for
improving genetic
potential of offspring.
• Perform complex analysis
of health, reproduction
status and recommends
operational measures.
17

18. 18
Blue River Technology
Used for thinning and
weeding of lettuce to
increase yield.
Advanced artificial
intelligence algorithms to
make plant-by-plant
decisions to optimize yield.
18

19. 19
Decision support system for greenhouse
tomato production
Determines day and night
air temperature, fruit
temperature, radiation,
CO2 concentration, fruit
load, plant density, stress
etc.
Adjusts fluctuation in
temperature during fruit
ripening.
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20. 20
Greenhouse climate controller with AI-based
techniques
Temperature and humidity
control through sensors linked
to a computer in artificially
conditioned green houses.
Compute the optimal values
for the set-points in the
green-house.
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21. 21
Gas fermentation system
Uses artificial intelligence to
predict the outcome of
fermentation process in cattle.
Provides diagnostic approach to
qualitative and quantitative data
on the rate of carbohydrate
digestion by cattle, during the
fermentation process in the
rumen of cattle.
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22. 22
GIGAS: Guelph intelligent greenhouse
automation system
It includes vision system
with multiple cameras.
Plant database keeps
track of all records and
DSS used, the planning is
sent to robot which
implement it.
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23. 23
Driverless Tractor
Software coupled with
sensors, radar and GPS
system.
Higher efficiency for
precision agriculture and
meet the labour shortage
at the farm.
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24. 24
Autonomous early warning system for fruit-fly
Build on wireless sensor
network and GSM
network to capture up-
to-the minute natural
environment fluctuation
on the fruit-farm.
Self-organizing maps and
support vector machines
are incorporated.
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25. 25
AI system
Learns from
experience
Uses the
learning to
a reason
Solves
complex
problems
Creates
perspectives
Artificial Intelligence System
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26. • It’s expensive
• Joblessness
• Hackers can exploit AI solutions to collect
private and sensitive information.
• The AI can be programmed to do something
devastating.
• If programmed to do something beneficial, but it
develops a destructive method for achieving its
goal, it can be dangerous. …
Limitations
27

27. …
• Strong AI would pass both Lovelace and the
Turing test, ie capable of doing things outside of
what it was programmed to do and be
indistinguishable from an ordinary human.
• Elon Musk and Stephen Hawking suspect that
some form of artificially intelligent humans are
bound to displace us according to the principle
of evolution.
• …
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28. • Large amount of data is needed to train AI, In
agriculture, spatial data are scarce, and much
during growing season only. It too requires pre-
processing (cleaning).
• The data infrastructure on the farm will need to
become more robust and IT equipped before large
scale agricultural AI deployment can be successful.
• AI is best suited for precision agriculture but may
see a more rapid adoption for development of new
seeds, fertilizers, or crop protection products.
Reason data of decades are available for agriculture
other than precision agriculture in India. …
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Limitations of AI in agriculture

29. …
• Problem is not the deployment and
development of AI, but is lack of likeness among
the two environments for validation and testing.
• AI and machine learning teaching is far away for
predicting critical outcomes in agriculture purely
through the cognitive abilities of machines.
• The problem with success of AI is not that they
not work well, but industry has not taken
sufficient time to respect that agriculture has
most uncertain environment to manage.
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30. • Adopt a deliberate policy to drive AI innovation,
adaptation and proliferation in all sectors
• Policymakers should make AI a critical
component flagship programmes such as Make
in India, Skill India and Digital India.
• The farmers should seek cognitive technologies
(eg. AI) to maximise return on crops
• Farmers should be directed to precision
agriculture, AI can be the best tool to assist it.
• AI should ensure active participation of farmers
to cope complexity in modern agriculture.
Recommendations
30

31. • 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 is quite useful in
agriculture.
• Artificial intelligence can be technological
revolution and boom in agriculture to feed the
increasing human population of world.
• Artificial intelligence will complement and
challenge to make right decision by farmers.
Conclusion
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32. • Brooks, R.A., 1986. Achieving Artificial Intelligence through Building
Robots (No. AI-M-899). MASSACHUSETTS INST OF TECH CAMBRIDGE
ARTIFICIAL INTELLIGENCE LAB.
• Sutton, R.S. and Barto, A.G., 1998. Introduction to reinforcement
learning (Vol. 135). Cambridge: MIT Press.
• Li, D. and Du, Y., 2007. Artificial intelligence with uncertainty. CRC press.
• Brooks, R.A., 1999. Cambrian intelligence: The early history of the new
AI (Vol. 97). Cambridge, MA: MIT press.
• Coulson, R.N., Folse, L.J. and Loh, D.K., 1987. Artificial intelligence and
natural resource management. Science, 237, pp.262-268.
• Papageorgiou, E.I., Stylios, C.D. and Groumpos, P.P., 2004. Active Hebbian
learning algorithm to train fuzzy cognitive maps. International journal of
approximate reasoning, 37(3), pp.219-249.
• Munakata, T., 1998. Fundamentals of the new artificial intelligence (Vol. 2).
Heidelberg: Springer.
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References

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