Worldwide, agriculture is a $5 trillion industry, and with the ever increasing population, the world will need to produce 50% more food by 2050 which cannot be accomplished with the percentage of land under cultivation. Factors such as climate change, population growth and food security concerns have propelled the industry into seeking more innovative approaches to protecting and improving crop yield. As a result, Artificial Intelligence is steadily emerging as part of the industry’s technological evolution which help can help farmers get more from the land while using resources more sustainably, yielding healthier crops, control pests, monitor soil, help with workload, etc *All the media belongs to the respective owners* Contact me for further queries & discussions...

1. Presentation by:
JANHVI TRIPATHI
BSc (Hons) Agriculture
Mentor: Ms Ardith Sankar
INSTITUTE OF AGRICULTURAL SCIENCES
BANARAS HINDU UNIVERSITY

2. WHAT IS ARTIFICIAL
ABOUT INTELLIGENCE
IN AI?

3. “
AI is the analytic process one can associate
with human thinking like speech recognition,
natural language understanding and
translation, knowledge management, image
analysis, decision making, learning etc. which
will make systems powerful and useful.
AI is a smart monitor system to find
solutions quickly
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4. AI IN AGRICULTURE
▪ Improve efficiency
▪ Reduce hostile environmental impacts
▪ The global AI in agriculture market is expected to be
worth USD 4.0 billion (currently USD 1 billion) by
2026 at a CAGR of 25.5%. [Markets and Markets Research]
America – major share
APAC market – highest CGAR
▪ Machine learning, computer vision and predictive
analytics
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5. CATEGORIES OF AI IN AGRICULTURE:
1. Agricultural robotics:
▪ can easily perform multiple tasks efficiently in the
farming field.
▪ Using computer vision to monitor weeds and spray
▪ Overcome labor challenge
▪ Assists in checking the crop quality
▪ Help farmers in picking/packing of crops
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For example: BLUE RIVER’S primary product, “SEE & SPRAY”,
uses computer vision, machine learning, and advanced robotic
technology to distinguish between crops and weeds — and then
spraying only the weeds. Precision spraying can help prevent
herbicide resistance

7. 2. Crop and soil monitoring:
▪ Remote Sensing + Hyper spectral imaging + 3D
Laser Scanning
▪ Spatial & Temporal information
▪ Detect soil defects, deficiencies, specific seed
reaction to different soils, weather impact, etc
▪ Cost saving, productivity gains
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8. Example:
▪ German-based tech startup PEAT PLANTIX
▫ Image recognition approach.
▫ Identifies the potential soil defects and nutrient deficiencies in
the soil including plant pests and diseases.
▫ Images captured by the camera and are matched with image
in server for diagnosis
▫ It even has a fertilizer calculator depending upon land size and
few cultivation tips crop wise.
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9. 3. Predictive analytics:
▪ It involves creating predictive models and digital intelligence
around a host of agro-parameters, including inputs, market
prices, allied services, such as credit and insurance, fintech,
logistics etc.
▪ Machine learning algorithms + Images captured by satellites
& drones Weather forecasting, Crop sustainability,
disease & pest identification, etc
For example: aWhere, an analytics company that harnesses
satellite data to provide intelligence on weather, soil, crop
health, etc.
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10. PRECISION FARMING
▪ Right Place, Right Time, Right input, Right amount, Right
way
▪ Accurate and controlled technique
▪ Crop rotation, optimum planting and harvesting time, etc
▪ Entire set of data generated from multiple sources needs to
be utilized as an input data for AI machine learning
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11. Global Positioning System:
continuous position
information in real time.
GPS receivers, either
carried to the field or
mounted on implements
Differential Global
Positioning System:
to improve GPS
accuracy, uses pseudo
range errors
Geographic
information systems:
hardware & software,
use feature attributes
and location data to
produce maps.
Remote Sensing
Collection of data from
a distance. Data
sensors can be hand
held devices, satellite
based etc
Variable Rate Applicator:
3 components: Control
computer, Locator,
Actuator
2 types: Map based VRA,
Sensor Based VRA
Combine harvesters
with yield monitors:
Yield monitors
continuously measure and
record the flow of grain in
combine harvester
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KEY TECHNOLOGIES OF PRECISION FARMING

12. WHY AI SHOULD BE USED?
1. Growth driven by IOT:
▪ Huge volumes of data on historical weather pattern, soil
reports, new research, images from Drones, etc get
generated. Cognitive IOT solutions can sense these
▪ Remote Sensing & Proximity Sensing
▪ Hardware solutions like Rowbot
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13. 2. Image-based insight generation:
▪ Drone-based images helps in in-depth field analysis, crop monitoring, field
scanning, real-time alerts; long-distance Aerial crop spraying with nutrients,
herbicide, high-efficiency crop analysis etc
▪ Recent PWC Study: total addressable market for Drone-based solutions -
$127.3 billion & for agriculture - $32.4 billion
▪ enables low cost of operation
▪ Detailed 3-D field map of terrain, drainage, soil viability and irrigation before
crop cycle.
i. Crop Readiness identification
ii. Field Management
iii. Disease Detection
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Input and Output Image of lemon leaf and output
Diseases is Sun burn disease
Input and Output Image of rose leaf and output
Diseases is bacterial leaf spot
Input and Output Image of banana leaf and output
Diseases is early scorch disease
Input and Output Image of beans leaf and output
Diseases is bacterial leaf spot

15. 3. Identification of optimal mix for agronomic products:
Recommendations on the best choice of crops and hybrid seeds.
4. Soil analysis & Crop health monitoring
5. Automation techniques in irrigation (Smart Irrigation):
▪ IoT based device
▪ Automation by analyzing Soil moisture & Climate condition
▪ Reduces drudgery
▪ Less productions costs.
▪ Saving water losses from agriculture
6. Decrease in Herbicide & Pesticide Usage :
▪ Identify weed infested area & Spray only where the weeds are
▪ Reduction in over pesticide & herbicide losses
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STATUS OF AI IN INDIAN AGRICULTURE

17. ▪ More than 500+ AgriTech start-ups in India.
Many of these start-ups are leveraging technologies
like AI, machine learning, etc
▪ According to the report by Hinrich Foundation, benefits
to Indian economy from digital trade has a potential to
grow to over $500 billion by 2030 from present $35
billion and agriculture is one of the 3 sectors that will
drive this growth. (2019)
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18. Yield Management using AI:
Artificial Intelligence (AI), Cloud Machine Learning, Satellite
Imagery & advanced analytics Smart Farming
1. Microsoft in collaboration with ICRISAT, developed an AI Sowing App
powered by Microsoft Cortana Intelligence Suite including Machine
Learning and Power BI.
▪ Gives sowing advisories for Groundnut, ragi, maize, cotton, rice etc
▪ No need to install any sensors
▪ 30% increase in average crop yield/hectare
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20. 2. Microsoft in collaboration with United Phosphorus
Limited Pest Risk Prediction API
(AI and machine learning )
▪ Based on the weather condition & crop growth
stage, pest attacks are predicted as:
▫ High
▫ Medium
▫ Low
▪ Reduces crop loss, thereby doubling farm income
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21. Example: IBM developed first of its kind in the country, an
advanced Price forecasting system for the Karnataka
Agricultural Prices Commission (KAPC)
▪ Predicts the market price trends for at least a fortnight and
the production pattern
▪ Also detects pest and disease infestations, estimate the
tomato output and yield.
▪ Initially launched for the 3 major tomato- growing districts
of Kolar, Chikkaballapur and Belgavi and 2 key maize-
producing districts of Davangere and Haveri.
 SatSure in India, assess imageries of farms and
predicts monetary prospects of their future yield.
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22. ADVANTAGES OF AI IN AGRICULTURE:
▪ Efficient ways to sustainably produce, harvest and sell essential
crops.
▪ Weather forecasting, disease & pest identification and improving
the potential for healthy crop production.
▪ Can improve crop management practices
▪ ML: recommend seeds, Automated machine adjustments, Weather
forecasting, Disease & pest identification, image recognition, etc
▪ Data and ML to spot trends, anomalies in purchasing or
consumption behavior, early warning when patterns change.
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23. ▪ Data can serve as currency for farmers.
▪ The collars are implanted with transponders : monitor
readiness of cow and automatic milking.
▪ AI practicing Precision Agriculture can help in optimizing
Agricultural inputs, prevent herbicide resistance etc
▪ A robotic lens which can predicts how long it will take for
the blossoms to become a ripe tomato ready for picking &
packing.
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24. DISADVANTAGES:
▪ Unemployment
▪ High cost of technology such as drones
▪ Can cost a lot of money and time to build, rebuild, and
repair
▪ Doesn’t improve with experience
▪ Lacks creativity
▪ Large amount of data is needed to train AI & Hackers
can exploit AI solutions to collect private & Sensitive data
▪ Cost of fuel to run technologies like Automatic robots,
etc
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25. PROSPECTS
1. Supply Chain Management
2. Combat global warming with expanded regulation of its
development.
3. Comprehensive automated solutions
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26. CHALLENGES TO AI IN AGRICULTURE
▪ Lack of familiarity with high tech machine learning
solutions
▪ AI also need data to train machines and to make
precise predictions.
▪ Bridging the gap between farmers and AI engineers
▪ The lack of implementation of a rural broadband
structure
▪ Privacy concerns of farmers in sharing data
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27. CONCLUSION
▪ Increase the capacity of production
▪ Reduce drudgery.
▪ Automation
▪ Accuracy
▪ Real time management
▪ Precision agriculture
▪ Cannot work outside of what they were programmed
▪ Lack the technical knowledge
▪ Need to be affordable, robust, viable & accessible to community
▪ Open source platform would make
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THANK YOU!