This document discusses how artificial intelligence can be used in agriculture to address challenges of increasing global food demand. It outlines how AI is being applied to automate farming activities, identify plant diseases, monitor crop quality and environmental factors. Specific AI applications mentioned include using machine learning on drone and satellite images to predict weather, analyze crop health and detect pests or deficiencies. Autonomous tractors and irrigation systems are discussed as ways AI can make farming more efficient by performing tasks with less labor and optimizing resource use. The conclusion states that AI can help resolve resource scarcity and complement farmer decision making to help feed a growing global population.

1. AI in AGRICULTURE
-SURYA S
IT-DEPT(STUDENT)
PRE-FINAL YEAR
VELS UNIVERSITY

2. INTRODUCTION
1.Artificial Intelligence is a branch of
computer science dealing with the
simulation of intelligent behaviour in
computers.
2.AI also be applied to any machine that
exhibits traits associated with a human
mind such as learning and problem-
solving.
3.The ideal characteristic of artificial
intelligence is its ability to rationalize and
take actions that have the best chance of
achieving a specific goal.

3. GREEN REVOLUTION
1. The global population is expected to
reach 10 billion people by 2050, which
mean double agricultural production in
order to meet food demands which is
about 70%increase in food production.
2. Farm enterprises require new and
innovative technologies to face and
overcome these challenges.
3. By using AI we can resolve the
challenges

4. HOW AI IS USED IN AGRICULTURE
1. Automated farming activities
2. Identi
fi
cation of plant disease before occurrenc
e
3. Managing crop qualit
y
4. Monitoring biotic and abiotic factor
s
5. Machine vision systems and phenotype leads to
adjustment
s
6.The growth in Arti
fi
cial Intelligence technology has
strengthened agro-based businesses to run more
ef
fi
ciently
.

5. SOIL and Crops Health Monitorin
g
1.Continues deforestation and degradation of soil
quality are becoming a big challenge for food
producing countries. But now a German-based tech
startup PEAT has developed a deep learning based
application called Plantix that can identify the
potential defects and nutrient de
fi
ciencies in the soil
including plant pests and diseases.
2.This app(PLANTIX) is working on image
recognition based technology and you can use you
your smartphone to capture the plant’s image and
detect the defects into the plants. You will also get
soil restoration techniques with tips and other
solutions on short videos on this app.

6. Precision Farming with Predictive
Analytic
s
1.AI applications in agriculture expanded into
doing the accurate and controlled farming through
providing proper guidance to farmers about
optimum planting, water management, crop
rotation, timely harvesting, nutrient management
and pest attacks.SkySquirrel Technologies acquired
by another similar company VineView brought
drone-based aerial imaging solutions for monitoring
crops health. A drone is used to make a round of
capturing the data from the vineyards
fi
eld and then
all the data is transferred via a USB drive from the
drone to a computer and analyzed by the experts.
2.While using the machine learning algorithms in
connection with images captured by satellites and
drones, AI-enabled technologies predict weather
conditions, analyze crop sustainability and evaluate
farms for the presence of diseases or pests and
poor plant nutrition on farms with data like
temperature, precipitation, wind speed, and solar
radiation.

7. Controlling Pest Infestation
s
Pests are one of the worst enemies of the farmers
damaging the crops globally before it is harvested
and stored for human consumption. Popular insects
like locusts, grasshoppers, and other insects are
eating the pro
fi
ts of farmers and gobbling the grains
meant for humans. But now AI in farming gives
growers a weapon against such bugs
.
AI and data companies are helping farmers to get
alert on his Smartphones about the grasshoppers
likely to descend towards a particular farm or
matured crop
fi
eld
.
AI companies using the new satellite images
against pictures of the same using historical data
and AI algorithm detects that the insects had
landed at another location and farmers use such
information after con
fi
rmation and timely remove
the costly pests from their
fi
elds.

8. Autonomous Tractor
s
1.With the heavy investment in developing
autonomous vehicles for various needs, the
agriculture sector will be also getting bene
fi
ts with
self-driving or you can say driverless tractors
.
With more quality AI and machine learning
training data for agriculture, the farm sector is
going to be revolutionized by the large scale use of
autonomous tractors for performing multiple tasks
.
2.These self-driving or driverless tractors are
programmed to independently detect their
ploughing position into the
fi
elds or decide the
speed and avoid obstacles like irrigation objects,
humans and animals while performing various
tasks.

9. AUTOMATED IRRIGATION
SYSTE
M
EFFECT OF USAGE
:
1. Reducing production cost of vegetables, making
the industries more competitive and sustainabl
e
2. Maintaining(or increasing) average vegetable
yield
s
3. Minimizing the environmental impacts caused
by excess applied water and subsequent
agrochemical leachin
g
4. Maintaining a desired soil water range in the
root zone that is optimal for plant growt
h
5. Low labour input for irrigation process
maintenanc
e
6. Substantial water saving compared to irrigation
management based on average historical
weather conditions.

10. CONCLUSIO
N
1. AI can be appropriate and ef
fi
cacious in
agriculture sector as it optimises the resource
use and ef
fi
cienc
y
2. It resolve the scarcity of resource and labour to
a large extent. Adoption of AI is quite useful in
agricultur
e
3. Arti
fi
cial Intelligence can be technological
revolution and boom in agriculture to feed the
increasing human population of world
4. Arti
fi
cial Intelligence will complement and
challenge to make right decision by farmers
.

11. REFERENCE
S
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of Sydney". 2016.*^ Craft, Andrew (1 March 2017). "Making it rain: Drones could be the future for cloud seeding". Fox
News. Retrieved 24 May 2017.*^ Anderson, Chris. "How Drones Came to Your Local Farm". MIT Technology Review.
Retrieved 24 May 2017.*^ Mazur, Michal. "Six Ways Drones Are Revolutionizing Agriculture". MIT Technology Review.
Retrieved 24 May 2017.^* https://www.veri
fi
edmarketresearch.com/product/global-agriculture-robots-market-size-and-
forecast-to-2025/*^ Belton, Padraig (2016-11-25). "In the future, will farming be fully automated?". BBC News. Retrieved
2016-11-28.*. ^ Monta, M.; Kondo, N.; Shibano, Y. (21–27 May 1995). Agricultural Robot in Grape Production System.
IEEE International Conference on Robotics and Automation. Nagoya: Institute of Electrical and Electronics Engineers.
pp. 2504–2509. doi:10.1109/ROBOT.1995.525635. ISBN 0-7803-1965-6. *^ Foglia, M. M.; Reina, G. (2006).
"Agricultural robot for radicchio harvesting" (PDF). Journal of Field Robotics. 23 (6–7): 363–377. doi:10.1002/rob.20131.
*^ Jump up to:
a b c d e Yaghoubi, S.; Akbarzadeh, N. A.; Bazargani, S. S.; Bazargani, S. S.; Bamizan, M.; Asl, M. I. (2013). "Autonomous
Robots for Agricultural Tasks and Farm Assignment and Future Trends in Agro Robots". International Journal of
Mechanical & Mechatronics Engineering. 13 (3): 1–6. CiteSeerX 10.1.1.418.3615
.
GUIDED B
Y
MRS. G.SUBASHIN
I
Asst.professo
r
IT dep
t
ST.Joesph institute of technology