AI-powered home security systems Secure-by-design IoT frameworks Government regulations (e.g., IoT Cybersecurity Act) User awareness campaigns

1. Leveraging AI for Smart
Agriculture
Explore AI's transformative role in agriculture. From precision
farming to crop yield predictions, AI-driven solutions are
revolutionizing the industry.
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Dr. Gururaj H L
Associate Professor & ACM Eminent Speaker
Manipal Institute Of Technology, Bengaluru
https://itsmeguru.in/

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3. Precision Farming with AI
Data-Driven Decisions
AI uses data analytics to optimize crop
management. Real-time data from drones and
sensors helps farmers make informed decisions.
Optimized Resources
Farmers use AI to improve irrigation. AI also
improves fertilization and pest control, enhancing
resource management.
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4. AI-Automated Irrigation
1 Weather Analysis
AI systems analyze
weather data for
optimal water usage.
2 Soil Monitoring
Systems monitor soil
moisture levels to
reduce water waste.
3 Plant Health
AI assesses plant health to improve crop yield sustainably.
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5. Crop Monitoring and
Disease Detection
Drone Imagery
Analyze images to
detect plant
diseases early.
Satellite Data
Use satellite imagery
to identify nutrient
deficiencies.
Pest Control
Detect pest infestations
to minimize potential
losses.
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6. AI-Powered Soil Analysis
Soil Composition
Assess soil composition with AI
tools.
pH Levels
Determine pH levels for optimal
growth.
Nutrient Content
Analyze nutrient content to
improve yield.
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7. Weather Prediction for
Climate Adaptation
AI Climate Models
Predict weather patterns
efficiently.
Efficient Planning
Plan planting and
harvesting schedules.
Loss Reduction
Minimize losses due to adverse weather.
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8. AI-Powered Robots in Agriculture
Weeding
Planting
Harvesting
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9. Crop Yield Prediction Models
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Regression Models
Predict crop output based on past data.
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Neural Networks
Analyze complex patterns to improve accuracy.
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Geospatial AI
Estimate crop health using satellite imagery.
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10. Challenges and Limitations
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Awareness
Education needed to utilize AI effectively.
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Connectivity
Internet access crucial for AI application.
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Data
Reliable data collection is key.
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Costs
Implementing AI requires investment.
AI in agriculture faces challenges, including high initial costs and the need for farmer training. Connectivity issues in rural areas and
data accuracy also pose limitations.
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11. Future of AI in Agriculture
The future of AI in agriculture is promising. Advancements in IoT
and blockchain integration will drive growth. AI-driven agritech
startups are developing cost-effective solutions for small-scale
farmers, enhancing food security and sustainable practices.
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33. Linear Regression for Yield Prediction
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34. LSTM for Rainfall Prediction
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35. Reinforcement Learning for Smart Irrigation
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42. THANK YOU
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Contact
gururaj.hl@manipal.edu
9686418942