Precision farming business model

2. Agriculture in Tamil Nadu – Current
Scenario
 60% rural population depends on agriculture
 Small & fragmented landholdings (avg. < 2 ha)
 Water scarcity, soil degradation, climate variability
 Need for sustainable, high-yield solutions
 Labour non-availability
 Input cost high
 Poor market network

3. Artificial Intelligence (AI)
 AI is a branch of computer science, dealing with the simulation of intelligent
behaviour in computers.
 AI is not a Man Vs Machine saga: Its in fact, Man with Machine synergy.

4. What is Precision Farming?
 Precision farming is a concept of doing “Right thing” in the “right
time”, at the “right place” with “right amount”.
 Site-specific crop management
 Use of data-driven decisions
 Optimize inputs (water, fertilizers, pesticides)
 Increase yield, reduce waste

5. Why AI in Agriculture?
 Traditional methods insufficient for modern challenges
 Big data + IoT sensors generate actionable insights
 AI enables predictive & prescriptive solutions
 Supports government policies (Digital India, TN e-Gov)
 More efficacy and optimise the resource use and efficiency.
 It solves scarcity of labour and resources to a larger extent.
 AI is the boom in agriculture to feed the increasing population of
world.

6. Role of AI in Precision Agriculture
 Data collection Processing Decision making Action
→ → →
 AI acts as the 'brain' of the farm ecosystem
 To increase production efficiency
 To improve product quality
 Effective use of all inputs
 To protect soil and water
 To determine the potential socio, economic and environmental
benefits.

7. AI in Crop Monitoring
 Satellite & drone imaging
 Disease detection via image recognition
 Growth stage monitoring with AI algorithms
 Automated farming activities
 Automated irrigation systems
 Identification of pest, disease and weeds out break weather
forecasting.

8. AI for Soil & Nutrient Management
 AI models predicting soil fertility
 Real-time nutrient recommendations
 Precision fertilizer application
 Grid soil sampling
 GIS based soil sampling
 Site specific Nutrient Management
 Soil survey and land use mapping (USDA) – by using remote sensing

9. AI for Irrigation Management
 Smart irrigation using AI + IoT sensors
 Predictive models for water needs
 Cauvery delta water scheduling
 Automative micro irrigation systems
 Laser land levelling
 Monitoring relative humidity and moisture stress
 Need based automated application of water at root zone
 Fertigation

10. AI in Pest & Disease Management
 Image-based detection of pests
 AI chatbots for farmers via mobile photos
 Reduces pesticide misuse
 Drone spraying
 Monitoring outbreak of pests before occurance

11. AI in Crop Yield Prediction
 Machine learning models for yield forecasting
 Helps in market planning & insurance
 Predicting paddy yield under monsoon variability
 Storage and warehousing
 Value addition
 Logistic support pre and post harvest
 Crop cutting experiments/NSSO

12. Robotics & Automation
 AI-powered drones for spraying
 Autonomous tractors & weed removers
 Labour shortage solution
 Automatic harvesters
 Automatic cleaner, graders and packers
 Transplanters , seed drills

13. AI in Supply Chain Optimization
 Predicting demand & price fluctuations
 Reducing post-harvest losses
 Connecting farmers to direct markets

14. Global Case Studies
 John Deere (AI tractors, precision seeding)
 IBM Watson Decision Platform for Agriculture
 Microsoft AI Sowing App

15. Indian Case Studies
 e-Choupal (ITC): digital farmer networks
 CropIn: AI-based farm management platform
 TNAU: AI in pest detection research
 e NAM – National Agricultural Marketing
 PMFBY – Pradhan Mantri Fasal Bima Yojana
 PMKVY – Pradhan Mantri Kaushal Vikas Yojana
 PM Kissan

16. Tamil Nadu Practices
 Drone spraying in Erode & Thanjavur
 AI-enabled soil testing in Salem
 Smart irrigation pilots in Coimbatore
 Remote Control (GIS) farming system in Pollachi, Sathyamangalam
 Lift irrigation systems cauvery basin.

17. Need for Business Models
 High cost of AI adoption
 Farmers’ limited capital
 Importance of sustainable revenue streams
 Employment generation
 Full utilization of all resources
 Bridge the gap between producer and consumer

18. Subscription Model
 Farmers pay for AI advisory as monthly/yearly subscription
 AI-based weather and crop advisory apps

19. Pay-Per-Use Model
 Drone spraying / soil testing charged per acre
 Reduces upfront investment

20. Platform-Based Model
 Aggregators connect farmers, buyers, and AI services
 Marketplace + advisory in single app
 Buyers and Sellers meet online
 e-Nam platform
 APMC Model and eNam model combination

21. Data-as-a-Service (DaaS)
 Farmer data sold to agri-input companies, insurers, policymakers
 Ethical data management & privacy concerns
 Market price for 52 weeks (Mapping)
 Data related to soil quality, water quality etc
 Cost for data and legal boundaries

22. Government & NGO Partnerships
 Subsidies for AI adoption
 Public–private partnerships
 CSR-based agri-digital platforms
 Farmer producer companies role
 Export and Import policies of Govt.
 Infrastructure support by NABARD, MOFPI, GOI and State Govt.
 AI based small training centres

23. Cooperative/Cluster Model
 FPOs adopt AI collectively
 Shared resources: drones, sensors, platforms
 Involvement of farmers organizations
 Co-operative or collective farming
 Crop wise clusters
 Marketing networks
 Successful models of Amul, Aavin etc.,

24. Startup Models in India
 AI-driven AgriTech startups (Ninjacart, Fasal, DeHaat)
 Venture capital support for agri innovation
 Tamil Nadu Startup & Innovation Mission
 Agri based business plans
 Creation of awareness among young and educated farmers.
 Linkages between incubation centers and endusers

25. Challenges in AI Adoption
 High cost of technology
 Low digital literacy among farmers
 Connectivity issues in rural Tamil Nadu
 Data privacy & ownership issues
 Risk and Uncertainty
 High salary packages for IT staff / skilled workers
 Network marketing

26. Opportunities for Tamil Nadu
 Strong research ecosystem (TNAU, IIT-M, Anna Univ)
 Government support for smart agriculture
 Export potential for AI-based solutions
 Job creation in AgriTech

27. Future of AI in Precision Farming
 Integration of AI + IoT + Robotics
 Cloud-based farm decision platforms
 AI-powered vertical farming & hydroponics
 Advancement in Micro irrigation
 Remote control of entire farming systems
 Quality assurance and precision post harvest technologies
 R&D for organic farming

28. Role of Academia & Research Scholars
 Localized AI models for TN crops
 Interdisciplinary research (AI + Agriculture + Economics + Management
+ Engineering)
 Industry & farmer collaborations
 Suggestion of innovative projects
 Guide the students about AI models and scope

29. Policy Recommendations
 Subsidies for AI adoption
 Skill development for farmers
 Open agri-data platforms for researchers
 Encouraging AgriTech startups
 Training and visit to successful Models
 More budgetary allotment for AI based research and development
 Inclusion of AI based syllabus in curriculum.

30. Key Takeaways
 AI transforms farming into data-driven enterprise
 Business models must ensure affordability & scalability
 Tamil Nadu can lead AI-driven agriculture

31. AI is not replacing farmers,
it is empowering them.
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