The document discusses the use of artificial intelligence in agriculture. It notes that the global population is expected to reach 10 billion by 2050, requiring a 70% increase in food production. AI can help address this challenge by improving yields, managing resources more efficiently, and automating farming activities. Specifically, the document outlines how AI is used for automated irrigation, remote crop monitoring using computer vision, harvesting of vine crops with multi-sensor data collection, decision support systems, driverless tractors, weed removal robots, and drone applications such as precision fertilizer planning and disease monitoring. The conclusion states that AI can optimize resource use and efficiency to help solve issues of scarcity and labor shortages in agriculture.

1. Sajib Chowdhury
Roll No: 502123011006
Year: 1st. Sem: 1st
Master of Technology
Department of Computer Science & Engineering,
Guru Nanak Institute of Technology
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Artificial Intelligence (AI) in Agriculture
By

2. Scope of AI in Agriculture
 According to the UN Food and Agriculture Organization. The population will
increase to 10 billion by 2050.
 Double agricultural production to meet food demands which is about a 70%
increase in food production.
 Only 4% of additional land will come by 2050.
 Farm enterprises require new and innovative technologies to face and overcome
these challenges.
 By usingAI we can resolve thesechallenges.
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3.  AI has a lot of direct applications across sectors.
 AI can also bring a paradigm shift in farming.
 AI-powered solutions enable farmers to do more with less. It will also improve
quality and yield.
 Agriculture is seeing rapid adoption of AI both in terms of agricultural products
and In-field farming techniques.
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4.  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.
 AI enabled drone in insect and pesticides spray
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HOWAI IS USED IN AGRICULTURE:

5. AUTOMATED IRRIGATION SYSTEM:
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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 labor input for irrigation process maintenance.
Substantial water saving compared to irrigation management based on average
historical weather conditions.

6. Source:-
Barman A., Neogi B., Pal S. (2020) Solar-Powered Automated IoT-Based Drip Irrigation System. In: Pattnaik P., Kumar R., Pal S., Panda S. (eds) IoT and Analytics for
Agriculture. Studies in Big Data, vol 63. Springer, Singapore. https://doi.org/10.1007/978-981-13-9177-4_2
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7. EFFECT OF USAGE
i)Reducing production costs of vegetables, making the industry more
competitive and sustainable.
ii)Maintaining (or increasing) average vegetable yields
iii)Minimizing environmental impacts caused by excess applied water and
subsequent agrichemical leaching.
iv)Maintaining a desired soil water range in the root zone that is optimal for
plant growth.
v)Low labor input for irrigation process maintenance
vi)Substantial water saving compared to irrigation management based on
average historical weather condition. 7

8. AI - REMOTE SENSING: CROP HEALTH MONITORING:
 Image-based Insight generation- Use of
Computer Visions Technology
 Field Management:
Using high-definition images from airborne
systems (drones or copters), real-time estimates
can be made during the cultivation period by
creating a field map and identifying areas where
crops require water, fertilizer, or pesticides.
This helps in resource optimization to a huge
extent.
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9. Image-based Insight generation- Use of
Computer Visions Technology….
• Disease detection:
• Preprocessing of the image ensures the leaf
images are segmented into areas like
background, non-diseased part, and
diseased part.
• It also helps in pest identification,
nutrient deficiency recognition, and
more.
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11.  Benefits:-
 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 tools.
 The trained use of hyperspectral imaging, spectroscopy, and/or 3D
mapping allows for a 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 values.
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12. 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 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
approach creates a
sensor collection
virtual world of
phenotype data in which all the crop
observables become mathematical values.
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13. DECISION SUPPORT SYSTEM (DSS) FOR FIELD
PREDICTION USING AI TECHNIQUES
 This system involves a set ofArtificial
Intelligence based techniques:
 Artificial Neural Networks (ANNs)
 GeneticAlgorithms (GAs)
 Grey System Theory
 (GST)
 Use of artificial intelligence-based
methods can offer a promising
approach to yield prediction and
compared favorably with traditional
methods.
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14. AI -DRIVER LESS TRACTOR
 Using ever-more sophisticated software coupled with off-the-shelf
technology including 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.
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15. 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
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16.  • 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 behind it.
 • It is also cheaper than the tools
currently used for weed-
elimination as it can work during
extended periods.
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17. Drones are being used in agriculture
Drone use in agriculture is growing as more farmers realise the technology’s
ability to perform key tasks and its fast-developing potential to take on bigger
roles in the future
Precision fertilizer programme planning
Nitrogen deficient areas in a crop can be clearly identified from above using
drones fitted with cameras that have enhanced sensors.
Weed and disease control programmes
Using similar techniques to the fertilizer planning, drone operators can
accurately assess weed and disease levels in arable crops.
Tree and land mapping
As well as the disease control aspect, orchard fruit growers can benefit from
reports on tree and row spacing with accurate calculations of canopy coverage.
Crop Spraying
Larger drones are already capable of applying small quantities of pesticide or
fertilizer to crops, orchards and forested areas 17

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19. CONCLUSION
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 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 ofAI 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.

20. T
hankYou
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