As agriculture increasingly turns to digital and AI-driven solutions to tackle inefficiencies, a fundamental question remains: Can AI truly democratize agricultural technology, making it accessible to all farmers, including smallholders? At the Agrotech Space-Reskilll Hackathon, held yesterday in Gurugram, industry leaders gathered for a thought-provoking panel discussion to explore the opportunities, challenges, and ethical considerations of integrating AI into Indian agriculture.
The event, which featured a hackathon and a panel discussion, was organized by Agrotech Space in partnership with Reskilll and supported by Microsoft Azure Development Community.
The panel discussion: “Can AI Democratize Technology for Farmers,” moderated by Suparnaa Chadda (Founder Curator of SABERA), featured Nitin Puri (Founder, KisaanSay), Sachin Sharma (Co-Founder, Agro Farm Ventures/Kisan Manch), Vishal Salgotra (CF & COO, INNOFarms.ai) and Sanjay Kapoor, (Founder, Agrotech Space). The panel examined how AI can be leveraged for supply chain efficiency, data-driven decision-making, and improved farmer profitability while also addressing issues of affordability, digital literacy, and data ownership.
AI as a Bridge, Not a Barrier
Setting the tone for the discussion, moderator Suparnaa Chadda emphasized the importance of ensuring AI is an enabler, not an obstacle, particularly for smallholder farmers who might lack the resources or skills to adopt new technologies. She pointed out that technology should serve the needs of the farmer first, rather than creating dependencies on expensive, inaccessible systems.
“AI has the potential to empower farmers by providing them with real-time insights, market intelligence, and better price predictability,” Chadda noted.
“But if AI remains exclusive to large agribusinesses, we risk deepening the digital divide. How do we ensure that AI doesn’t just become another tool of control but rather one of true empowerment?”
Throughout the discussion, she pushed the panellists to address the fundamental question of accessibility, challenging them to present real-world, scalable solutions that farmers can adopt today without major financial or educational barriers.
The Promise and Challenge of AI in Indian Agriculture
Nitin Puri spoke about how AI is already transforming global agriculture through climate forecasting, precision farming, and market intelligence. However, he acknowledged that while large agribusinesses are integrating AI tools rapidly, small and medium farmers often lack access to the same technologies due to cost constraints and lack of awareness.
“AI is not just about increasing efficiency; it’s about changing the way farmers interact with the agricultural ecosystem,” Puri said.
“But for AI to be truly democratic, we need to lower the barriers to access, whether it’s affordability, infrastructure, or digital literacy.”
Sachin Sharma highlighted the role of AI in solving post-harvest losses, one of the biggest challenges faced by Indian farmers. His company’s AI-driven optical grading systems for apples allow farmers to automatically classify their produce and receive accurate pricing based on size, colour, and quality, eliminating inefficiencies in manual sorting and reducing food wastage.
“Post-harvest losses are not just about storage failures,” Sharma explained.
“They are about inefficient pricing mechanisms, supply chain breakdowns, and lack of transparency in quality assessment. AI can play a critical role in ensuring that farmers are paid fairly for the true value of their produce.”
Who Owns the Farmer’s Data?
As AI becomes more integrated into agritech solutions, the panellists debated a pressing issue: Who owns the data? Vishal Salgotra pointed out that most AI-driven agritech platforms collect and centralize farmer data, which is then used to generate predictive models for farming practices. However, he raised concerns that this data is often controlled by corporations rather than the farmers themselves.
“Data is power, and if farmers don’t own their own data, they are not truly empowered. Instead, they become dependent on private companies for insights that they should have direct access to.”
He proposed a cooperative model, where farmers collectively own and control their data, ensuring they benefit from AI-driven insights without being locked into corporate-controlled systems.
Adding to this, Nitin Puri suggested that open-source agricultural data systems could be the key to making AI truly democratic, allowing farmers, cooperatives, and startups to access insights without expensive licensing fees.
The discussion then turned to blockchain technology as a potential solution for securing farmer data, with Sanjay Kapoor, Founder of Agrotech Space, noting that blockchain could provide a transparent, farmer-controlled mechanism for tracking agricultural transactions and insights.
“India is still in the early stages of digital transformation in agriculture, and more regulatory support is needed to ensure AI works for the farmer, not against them.”
Barriers to AI Adoption: Cost, Digital Literacy, and Trust
Despite AI’s vast potential, the panellists recognized significant hurdles in its widespread adoption.
One of the biggest challenges is cost. Many AI-driven solutions are expensive, making them inaccessible to smallholder farmers, who already struggle with slim profit margins. Sachin Sharma emphasized that while large agribusinesses are investing in AI-powered tools for soil testing, pest control, and yield prediction, smaller farmers need more financial support to integrate these technologies into their practices.
Digital literacy also remains a major challenge. While smartphone usage has increased, many farmers are still unfamiliar with digital tools and AI-driven applications. Nitin Puri emphasized that AI-based tools need to be user-friendly, available in regional languages, and designed for easy adoption by farmers without requiring extensive training.
Trust is another barrier. Many farmers remain sceptical of new technologies, fearing that AI could replace traditional agricultural knowledge rather than enhance it. The panellists agreed that AI must work alongside traditional farming methods, preserving generational wisdom while integrating data-driven insights.
Real-World Application of AI for Farmers
During the Q&A session, several participants raised thought-provoking questions about AI’s real-world implementation.
One participant asked, “If AI can predict market trends, why are farmers still struggling with price volatility?” To this, Nitin Puri responded that market-driven AI tools exist but are not yet widely accessible to small farmers. He suggested that government-backed AI price prediction platforms could be the key to reducing price fluctuations and ensuring farmers receive fair compensation.
Another participant questioned, “What incentives exist for farmers to adopt AI if they don’t see an immediate return on investment?” Sachin Sharma acknowledged that adoption incentives such as subsidies, cooperative models, and public-private partnerships are necessary to ease the financial burden on farmers and encourage them to integrate AI solutions gradually.
Can AI Be Truly Democratized?
As the discussion came to a close, the panellists offered their final thoughts on whether AI can truly democratize technology for farmers.
The consensus was clear—AI has the potential to transform agriculture, but only if it is implemented responsibly, affordably, and with the farmer’s best interests at heart. Public-private partnerships, regulatory frameworks, and AI solutions tailored specifically for smallholder farmers would be key in ensuring widespread adoption.
The panellists acknowledged the efforts of industry leaders, policymakers, and startups in bringing AI-driven solutions to Indian farmers. They emphasized that technology must work for the farmer, not just agribusinesses, and that true democratization of AI will only happen when access is affordable, transparent, and beneficial for all.