Leveraging Modern Technologies for Enhanced�Agricultural Practices: Application of Artificial�Intelligence, Drones, and Beyond

Leveraging Modern Technologies for Enhanced Agricultural Practices: Application of Artificial Intelligence, Drones, and Beyond

1. Leveraging Modern Technologies for Enhanced Agricultural Practices: Application of Artificial Intelligence, Drones, and Beyond Aditya Sinha Department of Extension Education Department of Plant Pathology Bihar Agricultural College, Sabour (Bhagalpur) 1

2. Introduction Artificial Intelligence is revolutionizing agriculture globally India, with its large agricultural sector, is seeing rapid adoption of AI-based solutions This study examines how AI is transforming Indian agriculture through innovative startups We aim to understand the impact, challenges, and future potential of these technologies 2

3. Research Objectives Examine how AI is leveraged by Indian agritech startups to address agricultural challenges Assess the impact of AI-driven solutions on agricultural productivity and sustainability Identify key success factors and challenges faced by these startups Provide insights for policymakers, investors, and entrepreneurs in the agritech sector 3

4. Research Design Mixed-method approach combining: Qualitative interviews of founders in newspapers/ media, farmers, and agricultural experts Quantitative analysis of startup performance metrics and user adoption rates Comparative analysis of business models and AI implementations Data collected over 6 months, covering diverse agricultural regions in India 4

5. Overview of studied startups CropIn : AI-powered farm management and monitoring Intello Labs : AI for produce quality assessment Fasal : Precision agriculture platform Tartansense : AI-powered robotic spraying SatSure : Satellite and AI-based crop monitoring and risk assessment 5

6. CropIn : AI-powered farm management and monitoring (SaaS-based) (2010, Bengaluru) Focus: Comprehensive farm management and monitoring AI applications: Satellite imagery analysis for crop health assessment Machine learning for yield prediction and risk management Key findings: 22% average increase in crop yield for partner farms Successfully implemented in over 50 crops across 30 countries Challenges in data collection in remote areas 6

7. CropIn: AI-powered farm management and monitoring (SaaS-based) 7

8. Intello Labs (2016; Gurugram, Haryana) Focus : Automated quality assessment of fruits and vegetables AI applications : Computer vision and deep learning for grading produce Real-time quality monitoring throughout the supply chain Key findings : Reduced post-harvest losses by up to 25% Improved price realization for farmers by 8-10% Facing challenges in standardizing quality metrics across diverse produce types 8

9. Fasal (2018, Bengaluru) Focus : AI-powered precision agriculture platform AI applications : IoT sensors and machine learning for farm-specific insights Predictive analytics for irrigation, pest control, and harvesting Key findings : Water usage reduction of up to 30% in pilot projects 20% increase in crop quality reported by users Adoption limited by initial setup costs and farmer training needs 9

10. Fasal 10

11. Tartansense / Niqo Robotics (2015, Bengaluru) Focus : AI-driven robotic solutions for precision spraying AI applications : Computer vision for weed detection Robotic systems for targeted herbicide application Key findings : Reduced herbicide usage by up to 70% in field trials Improved worker safety by minimizing chemical exposure Challenges in adapting robots to varied terrain and crop types 11

12. SatSure (2017, Bengaluru) Focus : Satellite-based crop monitoring and risk assessment AI applications : Machine learning algorithms for analyzing satellite imagery Predictive analytics for crop yield, health, and risk assessment Key findings : Provides insights for over 3 million acres of agricultural land in India Improved crop insurance accuracy and reduced claim processing time by 70% Challenges in obtaining high-resolution satellite data for small farm plots 12

13. Comparative analysis All startups leverage AI and advanced technologies, but with distinct focus areas CropIn and Fasal offer broader farm management solutions Intello Labs focuses on quality assessment SatSure provides macro-level insights through satellite imagery Tartansense unique in its hardware-software integration for robotic solutions Common thread : Making data-driven decision-making accessible to various stakeholders in agriculture 13

14. Success factors 14

15. Challenges 15

16. Impact on Indian Agriculture 16

17. Policy implications/ Recommendations 17

18. Future directions 18

19. Conclusion AI is playing a crucial role in addressing key challenges in Indian agriculture The studied startups demonstrate the diverse applications and potential of AI Success hinges on balancing technological innovation with on-ground realities Collaborative efforts between startups, government, and farmers are key AI in agriculture holds promise for improving food security and farmer livelihoods in India 19

20. Publication Green Agrevolution Pvt Ltd: delivering 360° “seed-to-market” solution Sinha, A., & Jha, S. (2019). Green Agrevolution Pvt Ltd: delivering 360°“seed-to-market” solution.  Emerald Emerging Markets Case Studies ,  9 (1), 1-23. DOI :  10.1108/EEMCS-06-2018-0148 20

21. Thank you for your attention! Q&A