Ekgaon’s Vijay Pratap Singh on India’s Agri Paradox: Data and AI Exist, Intent Doesn’t | NervNow<\/title>\n<link href=\"https:\/\/fonts.googleapis.com\/css2?family=DM+Serif+Display&family=Inter:wght@400;500;600&family=Source+Serif+4:wght@400;600&display=swap\" rel=\"stylesheet\">\n<style>\n \/* ============================================================\n ALL STYLES SCOPED TO .nervnow-article\n Nothing here will affect WordPress or any outside element.\n ============================================================ *\/\n\n .nervnow-article *,\n .nervnow-article *::before,\n .nervnow-article *::after {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n }\n\n .nervnow-article {\n --nn-navy: #182a4f;\n --nn-near-black: #0f0f0e;\n --nn-body-bg: #f9f8f5;\n --nn-rule: #d4cfc6;\n --nn-label: #7a7570;\n\n background: var(--nn-body-bg);\n color: var(--nn-near-black);\n font-family: 'Source Serif 4', serif;\n font-weight: 400;\n font-size: 18px;\n line-height: 1.75;\n }\n\n \/* ---- HEADER ---- *\/\n .nervnow-article .site-header {\n border-bottom: 2px solid var(--nn-navy);\n padding: 18px 0 14px;\n text-align: center;\n background: var(--nn-body-bg);\n }\n .nervnow-article .site-header .logo {\n font-family: 'Inter', sans-serif;\n font-weight: 600;\n font-size: 13px;\n letter-spacing: 0.18em;\n text-transform: uppercase;\n color: var(--nn-navy);\n text-decoration: none;\n }\n\n \/* ---- ARTICLE WRAPPER ---- *\/\n .nervnow-article .article-wrap {\n max-width: 740px;\n margin: 0 auto;\n padding: 56px 24px 80px;\n }\n\n \/* ---- KICKER \/ META ---- *\/\n .nervnow-article .kicker {\n font-family: 'Inter', sans-serif;\n font-size: 11px;\n font-weight: 600;\n letter-spacing: 0.18em;\n text-transform: uppercase;\n color: var(--nn-navy);\n margin-bottom: 20px;\n }\n\n \/* ---- HEADLINE \u2014 scoped h1, will not touch WordPress ---- *\/\n .nervnow-article h1 {\n font-family: 'DM Serif Display', serif;\n font-weight: 400;\n font-size: clamp(32px, 5vw, 48px);\n line-height: 1.15;\n color: #182a4f; 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More than two decades on, he argues that India’s AI problem in agriculture is not a problem of capability or data. It is a problem of will.<\/p>\n\n <div class=\"byline\">\n Interview by <strong>NervNow<\/strong> | Ekgaon Technologies\n <\/div>\n\n <p class=\"intro-graf\">Vijay Pratap Singh Aditya has watched UPI transform rural financial inclusion, India’s own navigation satellites go live, and its agricultural research institutions accumulate some of the world’s largest crop-science datasets. He has also watched all three remain almost entirely disconnected from the farmers who need them most. As co-founder and CEO of Ekgaon Technologies, which has worked directly with over one lakh farmers across approximately 5,000 villages, he occupies a rare vantage point: a technologist with two decades of ground-level experience in a sector that most AI discussions treat as an abstraction. NervNow spoke with him about what AI can actually do for Indian agriculture right now, why it is not doing it, and what the policy architecture that could change that looks like.<\/p>\n\n <div class=\"subject-bio\">\n <p class=\"name\">About the Speaker<\/p>\n <p class=\"title\">Vijay Pratap Singh Aditya is co-founder and CEO of Ekgaon Technologies, an Ashoka Fellow, co-founder of the Business Correspondent Federation of India (now merged into IAMAI), and has served on the board for sixteen years. Ekgaon was among the first for-profit enterprises globally to operate in the rural development sector, founding its mobile money infrastructure in 2002, a case study subsequently cited by the Reserve Bank of India in its first mobile banking guidelines in 2006.<\/p>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">Before we get to AI, it helps to understand the baseline. When you co-founded Ekgaon in 2002, what specifically were you trying to solve, and how much of that original vision has been achieved?<\/p>\n <div class=\"answer\">\n <p>The founding philosophy was rooted in Gandhi’s idea of gram swaraj, the self-reliant village ecosystem. Our tagline is “one village, one world network.” The logic is simple: the village is the lowest form of organized human existence, and if you can embed sustainability and self-reliance at that level, the same principle scales. We believed technology, specifically the internet, could enable that in 2002.<\/p>\n <p>When I reflect honestly after 23 years, the impact from one prism looks very limited. But from another prism, in terms of influence on policy and systems, it has been very large. Our mobile money work in 2002 led to a Reserve Bank of India case study that shaped the country’s first mobile banking guidelines in 2006. The Business Correspondent network I co-founded has grown and has now merged into IAMAI, which today represents over 90 lakh service providers and is the second-largest retail network in India after the Kirana store. Jan Dhan accounts, especially of women that were inactive, became active as the BC network grew and had more women agents serving women customers. These are large leveraged impacts, even if the direct footprint was small.<\/p>\n <p>But I am not satisfied. The scale of the problem and the scale of what technology could be doing are in completely different places.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"pull-quote\">\n <p>“We have satellites generating crop, weather, and soil data. We have 70 years of rainfall records. We have India’s own navigation satellite system. We are not using any of it to build AI tools for farmers. The data exists. The intent does not.”<\/p>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">Where specifically is AI being underused in Indian agriculture? Give us the clearest example you have.<\/p>\n <div class=\"answer\">\n <p>Weather prediction is the most obvious. India built some of the world’s most advanced space infrastructure for exactly this purpose, the Earth Observation (IRS\/Resourcesat\/Cartosat) and INSAT\/GSAT satellite series, lower Earth orbit satellites capturing agriculture data, weather data, soil temperature, rainfall patterns. For decades, that data was not being used domestically. We were physically selling it to American entities. The Weather Company, which is omnipresent in our phones as an app, was built on a combination of public science from NASA and NOAA and private analytics platforms. It built a profitable business using India’s own satellite data and sells weather services back into the Indian and other global markets.<\/p>\n <p>We also lacked the ground-truthing infrastructure, automatic weather stations at the village level, to complement the space-based data. Without that pairing, predictions in a tropical country like India, where local climatic variability is very high, were never going to be accurate enough. We have only now started investing in that. But we are still not running AI on the combined dataset to give farmers usable, granular weather forecasts.<\/p>\n <p>This is not a technology problem. AI can do this in its current form. The data is there. The infrastructure to process and distribute it is not being built.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">You campaigned for years to get Indian Meteorological Department rainfall data released into the public domain. What was the outcome, and what does that tell us about the structural barriers?<\/p>\n <div class=\"answer\">\n <p>The IMD was selling historical rainfall data until around 2012-13. The Government of India introduced the National Data Sharing and Accessibility Policy (NDSAP) in 2012, which eventually led to the release of that data in 2016. Since 2008 we had been paying for it to analyze rainfall patterns and advise farmers on crop switching. If rainfall in your area has been declining for ten years, you should not be growing paddy. You should be growing millets or pulses. That is not a complicated insight. It is a basic one. But the underlying data was paywalled.<\/p>\n <p>We ran a campaign writing to chief secretaries across states, to the central government, to the Prime Minister’s office. Eventually, seventy years of historical rainfall data was released into the public domain. That was a significant moment. But even now, you will not find Indian agricultural universities running systematic research on that data. The resource was freed. The analytical work has barely begun.<\/p>\n <p>If I build an AI engine tomorrow and feed it that rainfall data along with soil profiles and geo-position data, I can tell a farmer in which district that the cropping pattern needs to change, and to which crop. That is not rocket science with today’s tools. The question is why no institution is doing it at scale.<\/p>\n <\/div>\n <\/div>\n\n <hr class=\"section-rule\">\n\n <div class=\"qa-block\">\n <p class=\"question\">Ekgaon has been collecting 180 parameters of farm-level data for over two decades. Is that data actually AI-ready, and why has it not been used to build something at scale yet?<\/p>\n <div class=\"answer\">\n <p>The data we hold is AI-ready. We collect household data including family composition, assets, cattle, irrigation infrastructure. We collect crop data, cultivation practices, soil data, production volumes, and sale data. Based on those inputs, we provide site-specific advice on crop management, irrigation, and nutrient application. We are already running algorithms on real-time data to generate that advice.<\/p>\n <p>What we have not done is fully automate the system, where an AI engine runs continuously and pushes personalized recommendations directly to a farmer’s phone. The reason is not technical. It is usability. Our farmers are not yet using app-based tools the way you and I do. We built our mobile app eight years ago. Farmer adoption has stayed at two to four percent. The effective delivery channels remain voice calls and SMS. That tells you something important about where any AI-driven service for rural India actually needs to be deployed, not inside a polished app but through interfaces farmers already use and trust.<\/p>\n <p>The dataset itself is a small sample relative to India’s farmer population. Data collection at scale is expensive. That is why the government’s data infrastructure matters so much. Individual companies cannot solve this. The state has to build the public data layer.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">You mentioned UPI as the right model for what agriculture needs. What does an agricultural equivalent of UPI actually look like?<\/p>\n <div class=\"answer\">\n <p>UPI worked because the government built neutral, open infrastructure that private companies and individuals could use freely. No one is paying to use the UPI rails. Google Pay is not paying. Paytm is not paying. The infrastructure is provided as a public good, and innovation happens on top of it. The result is that India is now the largest digital payments network in the world. It activated hundreds of millions of dormant bank accounts. That is what a correctly scoped technology intervention looks like.<\/p>\n <p>Agriculture needs an Agriculture Data Corporation of India, structured similarly to the National Payments Corporation. This entity’s mandate would be to aggregate and freely publish government-held agriculture data: soil profiles, satellite imagery, weather data, water table data from the Central Ground Water Board, crop disease records from research stations, seventy years of rainfall history. Make all of it freely available to any developer, startup, or researcher through open APIs.<\/p>\n <p>Right now, startups are trying to build crop-disease detection tools or yield-prediction models without access to the datasets sitting in agricultural universities and research stations. India is the second-largest holder of agricultural research data in the world, after the United States. That data is not in public circulation. Release it, and you will see genuine innovation within a year.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"pull-quote\">\n <p>“86 to 87 percent of Indian farmers are small and marginal, as per the 2011 Census. That number would be higher now. Any AI solution that only works economically for large landholders is solving for, at most, four percent of the problem.”<\/p>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">Where do current agritech startups go wrong in their AI applications? You referenced drone-based crop assessment as one example.<\/p>\n <div class=\"answer\">\n <p>The unit economics problem is almost never discussed honestly. Take drone-based aerial photography for crop assessment. The technology works. AI can analyze the imagery to detect moisture stress, disease, or damage. The question is: who pays, and at what cost per farmer? If you send a technician with a drone to a half-acre plot and try to charge the farmer even 200 rupees for the service, you cannot cover the cost of the visit. The economics collapse at the individual farm level.<\/p>\n <p>But the same technology becomes viable if you shift the unit from one farmer to one Farmer Producer Organization, which might aggregate 500 to 1,000 farmers across a contiguous area. At that level, a single drone flight covers a meaningful geography. The FPO can pay an annual service fee. The unit economics work. The problem is that most startup founders are building for the individual farmer use case rather than the aggregated one.<\/p>\n <p>Now go one level further: if satellite data were freely available, the drone cost itself disappears. A developer only needs to run the AI inference layer. Charge 50 rupees per farmer per season for that analysis. It works. But it requires the government to treat satellite imagery as public infrastructure, the same way it treats the UPI network.<\/p>\n <p>As for the drone subsidy program the government has been running, I’ll say this directly. Distributing subsidized drones to rural youth without a revenue model for those drones is not an agriculture policy. It is a hardware distribution program. I spoke to a founder years ago who was building drone services for crop assessment. Two years later, he was flying drones at weddings to recover costs. That is not a reflection of his failure. It is a reflection of a policy that created a supply of technology with no corresponding use case or demand.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">You raised the issue of the feminization of agriculture as something almost no one in the policy room understood. What is actually happening, and what does it mean for how AI tools should be designed and deployed?<\/p>\n <div class=\"answer\">\n <p>Land parcels in India have been shrinking for three decades. The average holding per farmer is now around half an acre. As a result, men are leaving agriculture to seek wage work elsewhere, and women are taking over not just labor, but decision-making. What to grow, how to grow it, when to sell. This is happening without acknowledgment, without policy support, and without any corresponding adjustment in how technology or advisory services are delivered.<\/p>\n <p>The peer-learning channels are completely different for women in rural areas. A man will visit the pesticide shop, talk to other farmers at a tea stall, pick up advice through informal conversation. Women cannot access those networks. If you build a channel specifically targeting women farmers, in local languages, through voice or messaging, covering health impacts of pesticides, organic alternatives, crop selection, you will reach the actual decision-makers in Indian agriculture today. No agritech company I know is building for that demographic as their primary audience. Indian policymakers need to recognize that the feminization of agriculture is the new reality of India and the future of Indian agriculture, and adjust a policy framework that is well overdue for revision.<\/p>\n <\/div>\n <\/div>\n\n <hr class=\"section-rule\">\n\n <div class=\"qa-block\">\n <p class=\"question\">What is your overall assessment of the Agri Stack initiative the government has been working toward?<\/p>\n <div class=\"answer\">\n <p>I was approached in late 2020 by the Principal Scientific Advisor’s office and asked to contribute to the architecture. I prepared a detailed note, including which specific databases should be made publicly accessible and how the integration layer should be structured. The eventual output was a Kisan app that fell well short of what the sector needed. The validation metric the government used was download numbers. One million downloads was cited as success. The more meaningful measure is user engagement, and I would encourage anyone to look at that data on the Android Play Store themselves. Downloads are not usage.<\/p>\n <p>The concept behind Agri Stack is correct. A unified digital agriculture infrastructure that consolidates farmer registry data, land records, soil profiles, market price data, and weather data, accessible to developers through open APIs, is exactly what the sector needs. The execution has not matched the concept. The missing piece is not technical ambition. It is the institutional commitment to treat agricultural data as a public good rather than as a government asset to be protected or monetized.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">You have made the point that AI is an accelerator of good policy, not a substitute for it. Given everything you have described, what would actually move the needle?<\/p>\n <div class=\"answer\">\n <p>One person with the right institutional authority and the right understanding of the problem. I say this not as an abstraction. UPI was built during Raghuram Rajan’s tenure at the RBI, with his backing and institutional support behind it, and it changed financial inclusion for hundreds of millions of people within a decade. The technology existed. The policy architecture existed. What was needed was one person who understood the use case and had the mandate to execute it.<\/p>\n <p>The same is true for agriculture. The data exists. The AI capability exists. The satellite infrastructure exists. The research institutions hold decades of crop science. What does not exist is a decision-maker who understands all of this at once and has the authority to build the open data layer that unlocks it. When someone like that is in the right position, things will move fast. I am optimistic this will happen. Not because the current trajectory is promising, but because there is no other way. The sector cannot continue as it is.<\/p>\n <p>The Green Revolution happened because Jagjivan Ram came back from a humiliating meeting at the FAO in Rome and told M.S. Swaminathan: make India food independent, whatever it takes. Five years later, we were. That kind of institutional will, directed at the right problem with the right tools, is what it takes. The tools today are better than anything Swaminathan had. We are waiting for the will.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"read-more\">\n <h2>Read More Interviews<\/h2>\n <ul>\n <li><a href=\"https:\/\/nervnow.com\/ro\/sportsedtv-ceo-victor-bergonzoli-on-how-ai-is-turning-athlete-data-into-a-financial-risk-model\/\">SportsEdTV CEO Victor Bergonzoli on How AI is Turning Athlete Data Into a Financial Risk Model<\/a><\/li>\n <li><a href=\"https:\/\/nervnow.com\/ro\/dpdzero-founder-ranjith-br-on-what-10-million-ai-insights-reveal-about-debt-collections-in-india\/\">DPDzero Founder Ranjith BR on What 10 Million AI Insights Reveal About Debt Collections in India<\/a><\/li>\n <li><a href=\"https:\/\/nervnow.com\/ro\/georg-langlotz-ubs-india-does-not-have-an-ai-talent-problem-it-has-an-ai-ambition-problem\/\">Georg Langlotz, UBS: India Does Not Have an AI Talent Problem, It Has an AI Ambition Problem<\/a><\/li>\n <li><a href=\"https:\/\/nervnow.com\/ro\/lovables-whitney-menarcheck-on-ai-opportunity-community-first-thinking-and-more\/\">Lovable’s Whitney Menarcheck on AI Opportunity, Community-First Thinking, and More<\/a><\/li>\n <\/ul>\n <\/div>\n\n <div class=\"sources\">\n <h2>Sources<\/h2>\n <ol>\n <li>Reserve Bank of India, Mobile Banking Guidelines (2006) \u2014 cites Ekgaon case study. Available at rbi.org.in.<\/li>\n <li>Internet and Mobile Association of India (IAMAI), membership and BC network data.<\/li>\n <li>Government of India, Pradhan Mantri Jan Dhan Yojana, account activity data.<\/li>\n <li>Ekgaon Technologies, internal farmer data collection program, 180-parameter model.<\/li>\n <li>National Data Sharing and Accessibility Policy (NDSAP), Government of India, 2012. Indian Meteorological Department, historical rainfall data released to public domain, 2016.<\/li>\n <li>Ministry of Agriculture and Farmers Welfare, Agri Stack \/ Kisan app documentation.<\/li>\n <li>National Sample Survey, Agricultural Census data, 2005 and 2011 landholding size reports.<\/li>\n <\/ol>\n <\/div>\n\n <div class=\"article-footer\">\n This interview has been edited for clarity and length. Statements of opinion are those of the interviewee. NervNow covers AI and enterprise technology for senior decision-makers in India.\n <\/div>\n\n <\/div>\n\n<\/div>\n\n<\/body>\n<\/html>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>Ekgaon’s Vijay Pratap Singh Aditya argues that India’s AI problem in agriculture is not a problem of capability or data. 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{"id":6332,"date":"2026-04-17T12:15:57","date_gmt":"2026-04-17T06:45:57","guid":{"rendered":"https:\/\/nervnow.com\/?p=6332"},"modified":"2026-04-17T12:19:38","modified_gmt":"2026-04-17T06:49:38","slug":"ekgaons-vijay-pratap-singh-on-indias-agri-paradox-data-and-ai-exist-intent-doesnt","status":"publish","type":"post","link":"https:\/\/nervnow.com\/ro\/ekgaons-vijay-pratap-singh-on-indias-agri-paradox-data-and-ai-exist-intent-doesnt\/","title":{"rendered":"Ekgaon\u2019s Vijay Pratap Singh on India\u2019s Agri Paradox: Data and AI Exist, Intent Doesn\u2019t"},"content":{"rendered":"\n\n\n\n\nEkgaon’s Vijay Pratap Singh on India’s Agri Paradox: Data and AI Exist, Intent Doesn’t | NervNow<\/title>\n<link href=\"https:\/\/fonts.googleapis.com\/css2?family=DM+Serif+Display&family=Inter:wght@400;500;600&family=Source+Serif+4:wght@400;600&display=swap\" rel=\"stylesheet\">\n<style>\n \/* ============================================================\n ALL STYLES SCOPED TO .nervnow-article\n Nothing here will affect WordPress or any outside element.\n ============================================================ *\/\n\n .nervnow-article *,\n .nervnow-article *::before,\n .nervnow-article *::after {\n box-sizing: border-box;\n margin: 0;\n padding: 0;\n }\n\n .nervnow-article {\n --nn-navy: #182a4f;\n --nn-near-black: #0f0f0e;\n --nn-body-bg: #f9f8f5;\n --nn-rule: #d4cfc6;\n --nn-label: #7a7570;\n\n background: var(--nn-body-bg);\n color: var(--nn-near-black);\n font-family: 'Source Serif 4', serif;\n font-weight: 400;\n font-size: 18px;\n line-height: 1.75;\n }\n\n \/* ---- HEADER ---- *\/\n .nervnow-article .site-header {\n border-bottom: 2px solid var(--nn-navy);\n padding: 18px 0 14px;\n text-align: center;\n background: var(--nn-body-bg);\n }\n .nervnow-article .site-header .logo {\n font-family: 'Inter', sans-serif;\n font-weight: 600;\n font-size: 13px;\n letter-spacing: 0.18em;\n text-transform: uppercase;\n color: var(--nn-navy);\n text-decoration: none;\n }\n\n \/* ---- ARTICLE WRAPPER ---- *\/\n .nervnow-article .article-wrap {\n max-width: 740px;\n margin: 0 auto;\n padding: 56px 24px 80px;\n }\n\n \/* ---- KICKER \/ META ---- *\/\n .nervnow-article .kicker {\n font-family: 'Inter', sans-serif;\n font-size: 11px;\n font-weight: 600;\n letter-spacing: 0.18em;\n text-transform: uppercase;\n color: var(--nn-navy);\n margin-bottom: 20px;\n }\n\n \/* ---- HEADLINE \u2014 scoped h1, will not touch WordPress ---- *\/\n .nervnow-article h1 {\n font-family: 'DM Serif Display', serif;\n font-weight: 400;\n font-size: clamp(32px, 5vw, 48px);\n line-height: 1.15;\n color: #182a4f; 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\/* #182a4f *\/\n font-weight: 400;\n }\n\n \/* ---- DIVIDER ---- *\/\n .nervnow-article .section-rule {\n border: none;\n border-top: 1px solid var(--nn-rule);\n margin: 44px 0;\n }\n\n \/* ---- READ MORE ---- *\/\n .nervnow-article .read-more {\n margin-top: 60px;\n padding: 28px 0 32px;\n border-top: 2px solid var(--nn-navy);\n border-bottom: 1px solid var(--nn-rule);\n margin-bottom: 44px;\n }\n .nervnow-article .read-more h2 {\n font-family: 'Inter', sans-serif;\n font-size: 11px;\n font-weight: 600;\n letter-spacing: 0.16em;\n text-transform: uppercase;\n color: var(--nn-label);\n margin-bottom: 16px;\n }\n .nervnow-article .read-more ul {\n list-style: none;\n padding: 0;\n }\n .nervnow-article .read-more ul li {\n font-family: 'Inter', sans-serif;\n font-size: 14px;\n font-weight: 500;\n color: var(--nn-navy);\n padding: 10px 0;\n border-bottom: 1px solid var(--nn-rule);\n cursor: pointer;\n }\n .nervnow-article .read-more ul li:last-child {\n border-bottom: none;\n }\n .nervnow-article .read-more ul li::before {\n content: \"-- \";\n color: var(--nn-label);\n font-weight: 400;\n }\n .nervnow-article .read-more ul li a {\n color: var(--nn-navy);\n text-decoration: none;\n font-family: 'Inter', sans-serif;\n font-size: 14px;\n font-weight: 500;\n }\n .nervnow-article .read-more ul li a:hover {\n text-decoration: underline;\n }\n\n \/* ---- SOURCES ---- *\/\n .nervnow-article .sources {\n margin-top: 60px;\n padding-top: 24px;\n border-top: 2px solid var(--nn-navy);\n }\n .nervnow-article .sources h2 {\n font-family: 'Inter', sans-serif;\n font-size: 11px;\n font-weight: 600;\n letter-spacing: 0.16em;\n text-transform: uppercase;\n color: var(--nn-label);\n margin-bottom: 14px;\n }\n .nervnow-article .sources ol {\n padding-left: 20px;\n font-family: 'Inter', sans-serif;\n font-size: 13px;\n color: var(--nn-label);\n line-height: 1.8;\n }\n .nervnow-article .sources ol li {\n margin-bottom: 4px;\n }\n\n \/* ---- FOOTER ---- *\/\n .nervnow-article .article-footer {\n margin-top: 56px;\n padding-top: 24px;\n border-top: 1px solid var(--nn-rule);\n font-family: 'Inter', sans-serif;\n font-size: 12px;\n color: var(--nn-label);\n }\n<\/style>\n<\/head>\n<body>\n\n\n<div class=\"nervnow-article\">\n\n <header class=\"site-header\">\n <a class=\"logo\" href=\"#\">NervNow<\/a>\n <\/header>\n\n <div class=\"article-wrap\">\n\n <p class=\"kicker\">Interview | AI in Agriculture | Rural Technology<\/p>\n\n <h1>Ekgaon’s Vijay Pratap Singh on India’s Agri Paradox: Data and AI Exist, Intent Doesn’t<\/h1>\n\n <p class=\"deck\">Vijay Pratap Singh Aditya co-founded Ekgaon in 2002 to build technology for farmers before agritech was even a word. More than two decades on, he argues that India’s AI problem in agriculture is not a problem of capability or data. It is a problem of will.<\/p>\n\n <div class=\"byline\">\n Interview by <strong>NervNow<\/strong> | Ekgaon Technologies\n <\/div>\n\n <p class=\"intro-graf\">Vijay Pratap Singh Aditya has watched UPI transform rural financial inclusion, India’s own navigation satellites go live, and its agricultural research institutions accumulate some of the world’s largest crop-science datasets. He has also watched all three remain almost entirely disconnected from the farmers who need them most. As co-founder and CEO of Ekgaon Technologies, which has worked directly with over one lakh farmers across approximately 5,000 villages, he occupies a rare vantage point: a technologist with two decades of ground-level experience in a sector that most AI discussions treat as an abstraction. NervNow spoke with him about what AI can actually do for Indian agriculture right now, why it is not doing it, and what the policy architecture that could change that looks like.<\/p>\n\n <div class=\"subject-bio\">\n <p class=\"name\">About the Speaker<\/p>\n <p class=\"title\">Vijay Pratap Singh Aditya is co-founder and CEO of Ekgaon Technologies, an Ashoka Fellow, co-founder of the Business Correspondent Federation of India (now merged into IAMAI), and has served on the board for sixteen years. Ekgaon was among the first for-profit enterprises globally to operate in the rural development sector, founding its mobile money infrastructure in 2002, a case study subsequently cited by the Reserve Bank of India in its first mobile banking guidelines in 2006.<\/p>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">Before we get to AI, it helps to understand the baseline. When you co-founded Ekgaon in 2002, what specifically were you trying to solve, and how much of that original vision has been achieved?<\/p>\n <div class=\"answer\">\n <p>The founding philosophy was rooted in Gandhi’s idea of gram swaraj, the self-reliant village ecosystem. Our tagline is “one village, one world network.” The logic is simple: the village is the lowest form of organized human existence, and if you can embed sustainability and self-reliance at that level, the same principle scales. We believed technology, specifically the internet, could enable that in 2002.<\/p>\n <p>When I reflect honestly after 23 years, the impact from one prism looks very limited. But from another prism, in terms of influence on policy and systems, it has been very large. Our mobile money work in 2002 led to a Reserve Bank of India case study that shaped the country’s first mobile banking guidelines in 2006. The Business Correspondent network I co-founded has grown and has now merged into IAMAI, which today represents over 90 lakh service providers and is the second-largest retail network in India after the Kirana store. Jan Dhan accounts, especially of women that were inactive, became active as the BC network grew and had more women agents serving women customers. These are large leveraged impacts, even if the direct footprint was small.<\/p>\n <p>But I am not satisfied. The scale of the problem and the scale of what technology could be doing are in completely different places.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"pull-quote\">\n <p>“We have satellites generating crop, weather, and soil data. We have 70 years of rainfall records. We have India’s own navigation satellite system. We are not using any of it to build AI tools for farmers. The data exists. The intent does not.”<\/p>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">Where specifically is AI being underused in Indian agriculture? Give us the clearest example you have.<\/p>\n <div class=\"answer\">\n <p>Weather prediction is the most obvious. India built some of the world’s most advanced space infrastructure for exactly this purpose, the Earth Observation (IRS\/Resourcesat\/Cartosat) and INSAT\/GSAT satellite series, lower Earth orbit satellites capturing agriculture data, weather data, soil temperature, rainfall patterns. For decades, that data was not being used domestically. We were physically selling it to American entities. The Weather Company, which is omnipresent in our phones as an app, was built on a combination of public science from NASA and NOAA and private analytics platforms. It built a profitable business using India’s own satellite data and sells weather services back into the Indian and other global markets.<\/p>\n <p>We also lacked the ground-truthing infrastructure, automatic weather stations at the village level, to complement the space-based data. Without that pairing, predictions in a tropical country like India, where local climatic variability is very high, were never going to be accurate enough. We have only now started investing in that. But we are still not running AI on the combined dataset to give farmers usable, granular weather forecasts.<\/p>\n <p>This is not a technology problem. AI can do this in its current form. The data is there. The infrastructure to process and distribute it is not being built.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">You campaigned for years to get Indian Meteorological Department rainfall data released into the public domain. What was the outcome, and what does that tell us about the structural barriers?<\/p>\n <div class=\"answer\">\n <p>The IMD was selling historical rainfall data until around 2012-13. The Government of India introduced the National Data Sharing and Accessibility Policy (NDSAP) in 2012, which eventually led to the release of that data in 2016. Since 2008 we had been paying for it to analyze rainfall patterns and advise farmers on crop switching. If rainfall in your area has been declining for ten years, you should not be growing paddy. You should be growing millets or pulses. That is not a complicated insight. It is a basic one. But the underlying data was paywalled.<\/p>\n <p>We ran a campaign writing to chief secretaries across states, to the central government, to the Prime Minister’s office. Eventually, seventy years of historical rainfall data was released into the public domain. That was a significant moment. But even now, you will not find Indian agricultural universities running systematic research on that data. The resource was freed. The analytical work has barely begun.<\/p>\n <p>If I build an AI engine tomorrow and feed it that rainfall data along with soil profiles and geo-position data, I can tell a farmer in which district that the cropping pattern needs to change, and to which crop. That is not rocket science with today’s tools. The question is why no institution is doing it at scale.<\/p>\n <\/div>\n <\/div>\n\n <hr class=\"section-rule\">\n\n <div class=\"qa-block\">\n <p class=\"question\">Ekgaon has been collecting 180 parameters of farm-level data for over two decades. Is that data actually AI-ready, and why has it not been used to build something at scale yet?<\/p>\n <div class=\"answer\">\n <p>The data we hold is AI-ready. We collect household data including family composition, assets, cattle, irrigation infrastructure. We collect crop data, cultivation practices, soil data, production volumes, and sale data. Based on those inputs, we provide site-specific advice on crop management, irrigation, and nutrient application. We are already running algorithms on real-time data to generate that advice.<\/p>\n <p>What we have not done is fully automate the system, where an AI engine runs continuously and pushes personalized recommendations directly to a farmer’s phone. The reason is not technical. It is usability. Our farmers are not yet using app-based tools the way you and I do. We built our mobile app eight years ago. Farmer adoption has stayed at two to four percent. The effective delivery channels remain voice calls and SMS. That tells you something important about where any AI-driven service for rural India actually needs to be deployed, not inside a polished app but through interfaces farmers already use and trust.<\/p>\n <p>The dataset itself is a small sample relative to India’s farmer population. Data collection at scale is expensive. That is why the government’s data infrastructure matters so much. Individual companies cannot solve this. The state has to build the public data layer.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">You mentioned UPI as the right model for what agriculture needs. What does an agricultural equivalent of UPI actually look like?<\/p>\n <div class=\"answer\">\n <p>UPI worked because the government built neutral, open infrastructure that private companies and individuals could use freely. No one is paying to use the UPI rails. Google Pay is not paying. Paytm is not paying. The infrastructure is provided as a public good, and innovation happens on top of it. The result is that India is now the largest digital payments network in the world. It activated hundreds of millions of dormant bank accounts. That is what a correctly scoped technology intervention looks like.<\/p>\n <p>Agriculture needs an Agriculture Data Corporation of India, structured similarly to the National Payments Corporation. This entity’s mandate would be to aggregate and freely publish government-held agriculture data: soil profiles, satellite imagery, weather data, water table data from the Central Ground Water Board, crop disease records from research stations, seventy years of rainfall history. Make all of it freely available to any developer, startup, or researcher through open APIs.<\/p>\n <p>Right now, startups are trying to build crop-disease detection tools or yield-prediction models without access to the datasets sitting in agricultural universities and research stations. India is the second-largest holder of agricultural research data in the world, after the United States. That data is not in public circulation. Release it, and you will see genuine innovation within a year.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"pull-quote\">\n <p>“86 to 87 percent of Indian farmers are small and marginal, as per the 2011 Census. That number would be higher now. Any AI solution that only works economically for large landholders is solving for, at most, four percent of the problem.”<\/p>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">Where do current agritech startups go wrong in their AI applications? You referenced drone-based crop assessment as one example.<\/p>\n <div class=\"answer\">\n <p>The unit economics problem is almost never discussed honestly. Take drone-based aerial photography for crop assessment. The technology works. AI can analyze the imagery to detect moisture stress, disease, or damage. The question is: who pays, and at what cost per farmer? If you send a technician with a drone to a half-acre plot and try to charge the farmer even 200 rupees for the service, you cannot cover the cost of the visit. The economics collapse at the individual farm level.<\/p>\n <p>But the same technology becomes viable if you shift the unit from one farmer to one Farmer Producer Organization, which might aggregate 500 to 1,000 farmers across a contiguous area. At that level, a single drone flight covers a meaningful geography. The FPO can pay an annual service fee. The unit economics work. The problem is that most startup founders are building for the individual farmer use case rather than the aggregated one.<\/p>\n <p>Now go one level further: if satellite data were freely available, the drone cost itself disappears. A developer only needs to run the AI inference layer. Charge 50 rupees per farmer per season for that analysis. It works. But it requires the government to treat satellite imagery as public infrastructure, the same way it treats the UPI network.<\/p>\n <p>As for the drone subsidy program the government has been running, I’ll say this directly. Distributing subsidized drones to rural youth without a revenue model for those drones is not an agriculture policy. It is a hardware distribution program. I spoke to a founder years ago who was building drone services for crop assessment. Two years later, he was flying drones at weddings to recover costs. That is not a reflection of his failure. It is a reflection of a policy that created a supply of technology with no corresponding use case or demand.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">You raised the issue of the feminization of agriculture as something almost no one in the policy room understood. What is actually happening, and what does it mean for how AI tools should be designed and deployed?<\/p>\n <div class=\"answer\">\n <p>Land parcels in India have been shrinking for three decades. The average holding per farmer is now around half an acre. As a result, men are leaving agriculture to seek wage work elsewhere, and women are taking over not just labor, but decision-making. What to grow, how to grow it, when to sell. This is happening without acknowledgment, without policy support, and without any corresponding adjustment in how technology or advisory services are delivered.<\/p>\n <p>The peer-learning channels are completely different for women in rural areas. A man will visit the pesticide shop, talk to other farmers at a tea stall, pick up advice through informal conversation. Women cannot access those networks. If you build a channel specifically targeting women farmers, in local languages, through voice or messaging, covering health impacts of pesticides, organic alternatives, crop selection, you will reach the actual decision-makers in Indian agriculture today. No agritech company I know is building for that demographic as their primary audience. Indian policymakers need to recognize that the feminization of agriculture is the new reality of India and the future of Indian agriculture, and adjust a policy framework that is well overdue for revision.<\/p>\n <\/div>\n <\/div>\n\n <hr class=\"section-rule\">\n\n <div class=\"qa-block\">\n <p class=\"question\">What is your overall assessment of the Agri Stack initiative the government has been working toward?<\/p>\n <div class=\"answer\">\n <p>I was approached in late 2020 by the Principal Scientific Advisor’s office and asked to contribute to the architecture. I prepared a detailed note, including which specific databases should be made publicly accessible and how the integration layer should be structured. The eventual output was a Kisan app that fell well short of what the sector needed. The validation metric the government used was download numbers. One million downloads was cited as success. The more meaningful measure is user engagement, and I would encourage anyone to look at that data on the Android Play Store themselves. Downloads are not usage.<\/p>\n <p>The concept behind Agri Stack is correct. A unified digital agriculture infrastructure that consolidates farmer registry data, land records, soil profiles, market price data, and weather data, accessible to developers through open APIs, is exactly what the sector needs. The execution has not matched the concept. The missing piece is not technical ambition. It is the institutional commitment to treat agricultural data as a public good rather than as a government asset to be protected or monetized.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"qa-block\">\n <p class=\"question\">You have made the point that AI is an accelerator of good policy, not a substitute for it. Given everything you have described, what would actually move the needle?<\/p>\n <div class=\"answer\">\n <p>One person with the right institutional authority and the right understanding of the problem. I say this not as an abstraction. UPI was built during Raghuram Rajan’s tenure at the RBI, with his backing and institutional support behind it, and it changed financial inclusion for hundreds of millions of people within a decade. The technology existed. The policy architecture existed. What was needed was one person who understood the use case and had the mandate to execute it.<\/p>\n <p>The same is true for agriculture. The data exists. The AI capability exists. The satellite infrastructure exists. The research institutions hold decades of crop science. What does not exist is a decision-maker who understands all of this at once and has the authority to build the open data layer that unlocks it. When someone like that is in the right position, things will move fast. I am optimistic this will happen. Not because the current trajectory is promising, but because there is no other way. The sector cannot continue as it is.<\/p>\n <p>The Green Revolution happened because Jagjivan Ram came back from a humiliating meeting at the FAO in Rome and told M.S. Swaminathan: make India food independent, whatever it takes. Five years later, we were. That kind of institutional will, directed at the right problem with the right tools, is what it takes. The tools today are better than anything Swaminathan had. We are waiting for the will.<\/p>\n <\/div>\n <\/div>\n\n <div class=\"read-more\">\n <h2>Read More Interviews<\/h2>\n <ul>\n <li><a href=\"https:\/\/nervnow.com\/ro\/sportsedtv-ceo-victor-bergonzoli-on-how-ai-is-turning-athlete-data-into-a-financial-risk-model\/\">SportsEdTV CEO Victor Bergonzoli on How AI is Turning Athlete Data Into a Financial Risk Model<\/a><\/li>\n <li><a href=\"https:\/\/nervnow.com\/ro\/dpdzero-founder-ranjith-br-on-what-10-million-ai-insights-reveal-about-debt-collections-in-india\/\">DPDzero Founder Ranjith BR on What 10 Million AI Insights Reveal About Debt Collections in India<\/a><\/li>\n <li><a href=\"https:\/\/nervnow.com\/ro\/georg-langlotz-ubs-india-does-not-have-an-ai-talent-problem-it-has-an-ai-ambition-problem\/\">Georg Langlotz, UBS: India Does Not Have an AI Talent Problem, It Has an AI Ambition Problem<\/a><\/li>\n <li><a href=\"https:\/\/nervnow.com\/ro\/lovables-whitney-menarcheck-on-ai-opportunity-community-first-thinking-and-more\/\">Lovable’s Whitney Menarcheck on AI Opportunity, Community-First Thinking, and More<\/a><\/li>\n <\/ul>\n <\/div>\n\n <div class=\"sources\">\n <h2>Sources<\/h2>\n <ol>\n <li>Reserve Bank of India, Mobile Banking Guidelines (2006) \u2014 cites Ekgaon case study. Available at rbi.org.in.<\/li>\n <li>Internet and Mobile Association of India (IAMAI), membership and BC network data.<\/li>\n <li>Government of India, Pradhan Mantri Jan Dhan Yojana, account activity data.<\/li>\n <li>Ekgaon Technologies, internal farmer data collection program, 180-parameter model.<\/li>\n <li>National Data Sharing and Accessibility Policy (NDSAP), Government of India, 2012. Indian Meteorological Department, historical rainfall data released to public domain, 2016.<\/li>\n <li>Ministry of Agriculture and Farmers Welfare, Agri Stack \/ Kisan app documentation.<\/li>\n <li>National Sample Survey, Agricultural Census data, 2005 and 2011 landholding size reports.<\/li>\n <\/ol>\n <\/div>\n\n <div class=\"article-footer\">\n This interview has been edited for clarity and length. Statements of opinion are those of the interviewee. NervNow covers AI and enterprise technology for senior decision-makers in India.\n <\/div>\n\n <\/div>\n\n<\/div>\n\n<\/body>\n<\/html>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>Ekgaon’s Vijay Pratap Singh Aditya argues that India’s AI problem in agriculture is not a problem of capability or data. It is a problem of will.<\/p>","protected":false},"author":5,"featured_media":6267,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[106,95],"tags":[478,123],"class_list":["post-6332","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analysis-ai-technology","category-analysis","tag-agritech","tag-interview"],"blocksy_meta":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts\/6332","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/comments?post=6332"}],"version-history":[{"count":6,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts\/6332\/revisions"}],"predecessor-version":[{"id":6346,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/posts\/6332\/revisions\/6346"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/media\/6267"}],"wp:attachment":[{"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/media?parent=6332"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/categories?post=6332"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nervnow.com\/ro\/wp-json\/wp\/v2\/tags?post=6332"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}