AI_Precision_Farming_BusinessModels_Full.pptx

Precision farming business model

1. 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

2. 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.

3. 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

4. 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.

5. 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.

6. 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.

7. 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

8. 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

9. 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

10. 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

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

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

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

14. 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

15. 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.

16. 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

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

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

19. 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

20. 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

21. 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

22. 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.,

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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.

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

30. AI is not replacing farmers, it is empowering them. செயற்கை அறிந்தக கடைத்தும் உலகத்து இயற்கை அறிந்து செயல் --- குறள் * 63