PACIFIC COLLAGE OF AGRICULTURE
Affilated to pacific academy of higher education and research university
Debari Udaipur Rajasthan 313003
Dr. G.L. Sharma
Prof. & Head (Agronomy)
Artificial Intelligence in Agriculture
Artificial intelligence is a branch of computer science dealing
with the simulation of intelligent behavior in computer
Artificial intelligence is not a man versus machine saga; it is in
fact man with machine synergy.
Now let us proceed foreseeing how AI can help the common
main in the year to come . Technology has advanced at such an
accelerating pace that what has been achieved during the past
15 years or so from the early 2000s, is much more than what
has been achieved during the 30 years past, 1970s. In this era,
we have computers in our pockets now connected to the
internet giving us a plethora of options like streaming videos
and information in our finger tips . AI is like everything else has
two sides, one, there are practical applications of AI, on the
other hand it stands out being the robot,
The global population is expected to reach 10 billion people
by 2050, Which means double agriculture producation in
order to meet food demands which is about 70% increase in
Farm enterprises require new and innovative technologies to
face and overcome these challenges.
By using A I we can resolve these challenges.
HOW A I IS USED IN AGRICULTURE
Automated farming activities.
Identification of pest and disease outbreak before occurrence .
Managing crop quality.
Abiotic factors and stress.
Machine vision system and phenotype lead to adjustments.
• Levels of artificial
• There are three types of AI: ANI, AGI, and ASI.
• ANI (Artificial Narrow Intelligence) - It is the
first level that can make a decade only in one
sphere. Is all over the place, like Google maps.
• AGI (Artificial General Intelligence) -It has
the ability to reason, plan, solve problems,
think abstractly, comprehend complex ideas,
Learn quickly , and learn from experience.
• ASI (Artificial Super Intelligence)- this would
be when a computer or a system is better than
a human being wiser, more creative, more
socially adept, and this ranges from being a
little bit better to being smarter than the sum of
all humanity combined.
• Artificial Intelligence (AI) in Agriculture
• Government of India has already prioritized
• doubling farmer’s income as a national
• agenda, and putting a considerable focus on
• supply chain perspectives in agriculture and
• market development.
• Natural Language Processing (NLP), Robotics,
• Machine learning (ML), Automated
• Reasoning, Knowledge representation, Expert
• systems, computer vision, Speech recognition,
• Automated data analytics, Virtual reality,
• Augmented reality, Internet of things (IoT),
• Cloud computing, Statistical computing, Deep
• learning etc. are some major sub areas of AI. It
• is having huge potential in solving complex
• problems of agriculture.
• Role of Artificial Intelligence in Agriculture Sector
• The Internet of things (IoT) driven development for easy transfer of
information regarding : weather pattern, soil reports, new research,
pattern, soil reports, new research, pattern, soil reports, new
research, rainfall, vulnerability to pest attack, imaging through drones
• Disease detection: The image sensing and analysis ensure that the
plant leaf images are sectioned into surface areas like background,
diseased area and non-diseased area of the leaf.
• Identify the readiness of the crop: Images of various crops captured
under white light and UVA light are to check how ripe the green fruits
are from this analysis the farmer could create different levels on the
readiness of the fruits or crop category.
• Field management: By using images of high definition from drone
and copters systems real time estimations Can be attained during
the time span of cultivation by building a field map and discovering
areas where the crops require water, fertilizer and pesticides. The
optimization of resource is assisted to a huge extent by this.
• AUTOMATED IRRIGATION SYSTEM
• EFFECT OF USAGE:
• Reducing production costs of vegetables making the industry more competitive and sustainable.
• Maintaining (or increasing) average vegetable yields.
• Minimizing environmental impacts caused by excess applied water and subsequent agrichemical leaching.
• Maintaining a desired soil water range in the root zone that is optimal for plant growth.
• Low labour input for irrigation process maintenance
• Substantial water saving compared to irrigation management based on average historical weather conditions.
• AI-REMOTE SENSING: CROP HEALTH MONITORING:
• Hyperspectral imaging and 3D laser scanning are
capable of rapidly providing enhanced information
and plant metrics across thousands of acres with the
spatial resolution to delineate individual plots and or
plants and the temporal advantage of tracking
changes throughout the growing cycle.
Conventional methods are often time consuming and generally
categorical in contrast to what can be analysis technologies
categorized as remote sensing tools.
The trained use of hyperspectral imaging spectroscopy and/
or 3D mapping allows for the substantial increase in the
number of scalable physical observables in the field.
In effect the multi sensor collection approach creates a virtual
world of phenotype data in which all the crop observables
becomes mathematical values.
AI FOR HARVESTING VINE CROPS
Conventional methods are often time
consuming and generally categorical in
contrast to what can be analyzed through
automated digital detection and analysis
technologies categorized as remote sensing
The trained use of hyperspectral imaging,
spectroscopy and/or 3D mapping allows for the
substantial increase in the number of scalable
physical observables in the field In effect, the
multi sensor collection approach creates a
virtual world of phenotype data in which all
the crop observables become mathematical
AI FOR AUTONOMOUS EARLY WARNING
SYSTEM FOR ORIENTAL FRUIT FLY
(BACTROCERA DORSALIS) OUTBREAKS
• This autonomous early warning system,
built upon the basis of wireless sensor
networks and GSM networks effectively
captures long-term and up-to-the-minute
natural environmental fluctuations in fruit
• In addition, two machine learning
techniques, self- organizing maps and
support vector machines, are incorporated
to perform adaptive learning and
automatically issue a warning message to
farmers and government officials via GSM
DECISION SUPPORT SYSTEM (DSS) FOR FIELD
PREDICTION USING AI TECHNIQUES
• This system involves a set of Artificial
Intelligence based techniques
• Artificial Neural Networks(ANNs)
• Genetic Algorithms (GAS) Grey System Theory
• Use of artificial intelligence based methods
can offer a promising approach to yield
prediction and compared favorably with
• Using ever-more
coupled with off-the-shelf
sensors, radar, and GPS, the
system allows an operator
working a combine to set
the course of a driverless
tractor pulling a grain cart,
position the cart to receive
the grain from the combine,
and then send the fully
loaded cart to be unloaded.
AI FOR WEEDING
• The Hortibot is about 3-foot-by-3- foot,
is self-propelled, and uses global
positioning system (GPS).
• It can recognize 25 different kinds of
weeds and eliminate them by using its
weed- removing attachments
HortiBotis eco-friendly, because it sprays
exactly above the weeds.
As the machine is light --between 200 and
300 kilograms --so it will not hurt the soil
It is also cheaper than the tools currently
used for weed-elimination as it can work
during extended periods of time.
Al 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 alarge extent.
Adoption of Al 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.