The document discusses the role of computer vision in enhancing smart agriculture and precision farming, highlighting the need for improved real-time vision models and automation to tackle challenges such as inefficiency in traditional farming and accurate crop health assessment. It outlines proposed methodologies for image acquisition and analysis, emphasizing the potential of AI to optimize yields and reduce labor costs. Lastly, the document addresses future developments in AI-driven automation, environmental sustainability, and scalability for large-scale farming.

1. Computer Vision in Smart Agriculture and Precision
Farming: Techniques and Applications
Sumaira Ghaza1 Arslan Munir2 Waqar S. Qureshi3
1Department of Computer Science, Kansas State University, 66506 Manhattan, KS, USA
2Department of Electrical Engineering and Computer Science, Florida Atlantic University,
33431 Boca Raton, FL, USA
3School of Computer Science, University of Galway, H91 TK33 Galway, Ireland
Presented By: Sudhanshu Singh
October 23, 2024
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2. Outline
Introduction
Research Background
Research Need
Problem Statement
Analysis
Conclusion
Future Work
Sudhanshu Singh(IIITL) Computer Vision in Smart Agriculture October 23, 2024 2/11

3. Research Background
Digitization in agriculture
AI, Computer Vision driven growth
Focus: precision farming techniques
Key stages: image acquisition, analysis
AI boosts productivity, sustainability
Tackles food security, global issues
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4. Research Needs
Improved real-time vision-based models needed
Integration with diverse crop environments required
Enhance precision in stress detection techniques
Develop scalable solutions for large farms
Improve data fusion from multiple sensors
Focus on sustainability and reduced resource use
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5. Problem Statement
Traditional farming lacks precision, efficiency
Manual monitoring is time-consuming and inconsistent
Diverse environmental conditions challenge uniform solutions
Accurate crop health assessment remains difficult
Real-time data processing is limited in agriculture
Need for automation to reduce labor costs
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6. Proposed Methodology
Image Acquisition Systems
Image Stitching Techniques
Image Analysis for Crops
Decision Making using AI
Automated Treatment Plans
Real-Time Implementation Focus
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7. Proposed Methodology via Flow Chart
Figure.1 : Digital life cycle of crops in precision agriculture
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8. Analysis
Enhanced soil characterization for better fertilizer decisions
Early detection of crop diseases and nutrient issues
Data integration for optimized resource usage
Targeted treatments increase yield and quality
Automation reduces labor costs and monitoring time
Addressing model challenges is crucial for success
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9. Conclusion
AI revolutionizes precision agriculture applications
Vision systems automate crop monitoring effectively
RGB, multispectral, thermal imaging widely used
Key for detecting stress, yield optimization
Real-time deployment poses major challenges
Future: autonomous, scalable agricultural solutions
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10. Future Scope
Expand AI-driven automation in agriculture
Develop adaptive models for diverse crops
Improve real-time decision-making with AI
Integrate advanced robotics for autonomous farming
Enhance environmental sustainability through precision
Increase scalability for large-scale farming operations
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11. Thank You
Sudhanshu Singh(IIITL) Computer Vision in Smart Agriculture October 23, 2024 11/11