India’s agritech sector is entering a high-stakes phase, powered by policy boosts, capital flows, and the rapid rise of AI and blockchain on the farm. A 15 per cent hike in the agriculture budget and RBI’s rate cut have primed the sector for digital disruption. At Agrospectrum India’s recent webinar, leaders from Infosys, Sahyadri Farms, Cropin, Forvis Mazars and Farm2Fam showcased how smart technologies are already transforming everything from grape exports to weather-triggered interventions. The shift is no longer about pilot projects—it’s about building scalable, farmer-first platforms that work in real-world conditions. But tech alone won’t deliver impact without trust, usability, and cross-sector collaboration. If India gets it right, its agri-digital revolution could become a global blueprint.

India’s agritech sector is entering a defining phase in 2025, propelled by a confluence of strategic policymaking, technological leapfrogging, and heightened capital flows. In a clear signal of intent, the government has increased the agriculture budget by 15 per cent to Rs 1.73 lakh crore, with a sharp focus on embedding artificial intelligence, IoT, and blockchain into the fabric of Indian agriculture. The objective is unambiguous: Boost farm incomes, smoothen price volatility, and catalyse a tech-led transformation that could reset the productivity curve for the sector.

India’s agritech sector is at a critical inflexion point, propelled by strategic policy shifts, accommodative monetary moves, and rapid technological adoption. The RBI’s recent repo rate cut to 6.25 per cent has eased liquidity, catalysing funding flows into agritech startups and innovation-led ventures. Concurrently, trade negotiations with the US and EU are poised to unlock new export corridors for Indian agritech products, bolstering investor sentiment and global competitiveness. However, structural challenges remain. Supply chain bottlenecks, high import duties on advanced equipment, and a deepening talent deficit in emerging tech continue to constrain scalability.

The industry faces an urgent need for skilled professionals adept in AI, IoT, and data analytics—critical enablers of the next leap in farm productivity. Despite these hurdles, the shift towards precision agriculture is unmistakable. From AI-driven crop analytics to drone-enabled field monitoring, Indian farms are beginning to integrate smart solutions that enhance efficiency, optimise inputs, and build climate resilience. As digital tools become central to farm operations, they are not just augmenting yields—they are reshaping the very architecture of Indian agriculture.

Against this background, Agrospectrum India organised a webinar titled- From Algorithms to Agriculture: The AI Advantage, aimed at highlighting the real-world breakthroughs happening on farms in current times. The initiative deep dives into the next generation of agricultural technology, thereby integrating AI, data analytics, IoT, robotics, and cloud computing to create smarter, more sustainable, and more productive farming systems. The idea is to go beyond precision farming to offer predictive, adaptive, and automated solutions for every stage of the farming lifecycle – AI being the engine driving it all.

Speaking at the webinar, Ramachandran Sundaram, Principal Consultant, Infosys, spoke on aiming for the maturity level to leverage AI in agriculture, highlighting upon promise, prudence, and the path to maturity. “AI isn’t plug-and-play. It requires groundwork—starting with data,” he said. Drawing a parallel with Industry 4.0 in manufacturing, Ramachandran outlined a four-stage framework for AI maturity in agriculture: collecting data via IoT sensors, analysing it for actionable insights, simulating scenarios using digital twins, and only then applying AI for real-time, optimised decision-making. He highlighted the challenges of scaling data collection across large farms, including data heterogeneity, volume, and processing needs—areas where cloud technologies play a critical role.

“The concept of Industry 4.0, the fourth industrial revolution, offers us a useful framework—one that is technology-agnostic and highly relevant to agriculture. At the core of AI lies data. Data is the raw material that fuels every AI model. And in agriculture, data collection typically starts with IoT sensors and connected devices in the field. These tools provide visibility—they tell us what’s happening on the farm in real time” — Ramachandran Sundaram, Principal Consultant, Infosys

Infosys, he noted, has developed a cloud-based platform, Agrigam, tailored for agricultural applications. Emphasising that decisions made using AI in farming are often costly and irreversible, he underscored the importance of simulating “what-if” scenarios before deploying interventions on the ground. At the highest level of maturity, AI can detect unseen patterns, predict crop issues, and enable autonomous, real-time decisions. Ramachandran concluded with a clear message: “Don’t jump straight to AI. Build the foundation first. When used right, it can fundamentally reshape Indian agriculture.”

Vilas Shinde, Founder & Managing Director, Sahyadri Farms, highlighted how India’s table grape sector eyes a global edge with tech-driven traceability and brand push. India’s grape sector is gearing up for its next leap in global competitiveness, with leading industry voices highlighting the critical role of technology, traceability, and brand-building in sustaining export momentum and expanding domestic market share.

“India ranks as the fourth-largest exporter of grapes globally, competing with established players such as South Africa, Chile, and Peru. What makes this achievement even more remarkable is that we are doing this with highly fragmented land holdings. It’s a clear reflection of the power of technology-enabled ecosystems that span the entire grape value chain ” — Vilas Shinde, Founder & Managing Director, Sahyadri Farms

Shinde mentioned that while India has emerged as the world’s fourth-largest exporter of grapes, it must now move beyond volume to focus on quality assurance and consumer trust. “Climate change, pest pressure, and food safety norms—especially from Europe—are raising the bar for Indian exporters. What gives us an edge is our ability to use technology to ensure traceability and compliance, even with fragmented landholdings,” Shinde mentioned. India’s journey toward digitised grape farming began in 2004 with APEDA’s GrapeNet, the first crop-specific digital traceability system in the country.

Two decades on, producers like Sahyadri Farms have built on that foundation, developing in-house digital ecosystems that capture real-time, plot-level data on crop management, chemical use, and harvest parameters. Sahyadri, which works with over 14,000 acres under grape cultivation, has connected each plot to a common digital backbone, enabling residue tracking, export compliance, and quality benchmarking. This has allowed smallholder farmers to participate in high-value global supply chains without compromising on traceability or food safety. “This model of technology-powered collectivisation not only meets global standards but also opens the door for India to create strong, consumer-facing brands in horticulture—something the sector has lacked so far,” Shinde said, drawing parallels with India’s success in dairy branding through cooperatives like Amul.

As global retailers tighten procurement norms and demand data-backed assurances on quality, Indian grape exporters are betting that digital tools and integrated farm-to-shelf models will be key to securing long-term contracts and higher price realisations.

“Look at what countries like the US have done with blueberries. A unified identity, consistent quality, and data-backed traceability have allowed them to command a premium and dominate shelf space. Today, whether we are technologists, producers, exporters, or innovators, we’re all working toward the same goal: positioning India as a trusted, reliable, and premium origin for horticulture. The path to that goal runs through technology. AI and blockchain aren’t just enablers—they’re equalisers. They allow small and large growers alike to meet global benchmarks, standardise quality, and earn better prices ” — Keya Salot, Co-Founder, Farm2Fam India Private Limited

Adding momentum to the discussion, Keya Salot, Co-Founder, Farm2Fam India Private Limited, enlightened the audience on how blockchain and AI power India’s push for global leadership in horticulture. Speaking at the webinar, Keya said that while blockchain is widely used in finance and logistics, its full potential remains untapped in Indian horticulture. “It’s high time we mainstream these technologies to solve fundamental issues like traceability, accountability, and compliance,” she said. Blockchain, she explained, can record every step of the production and post-harvest process in a tamper-proof, time-stamped manner—from sowing and nutrition schedules to spray cycles, harvest timelines, and cold chain integrity.

“If a cold chain fails due to a breakdown or mishandling by a logistics partner, blockchain-backed systems can pinpoint the lapse and its impact on quality and shelf life,” Keya noted. In tandem, artificial intelligence is emerging as a powerful tool for predictive farm management. By integrating weather data and on-ground sensor inputs, AI can alert farmers to conditions such as upcoming dry spells or humidity drops, enabling proactive measures like deploying foggers or adjusting nutrition. “This is already being done globally. India must now scale it,” she added.

Beyond productivity and risk management, Keya emphasised that these technologies also simplify global compliance processes. Automated data entry and real-time monitoring of inputs can help producers meet stringent Maximum Residue Limits (MRLs) without extensive paperwork, a key barrier for many exporters. Ultimately, the bigger vision, Keya argued, is to build Brand India in the global fresh produce market, on par with how the U.S. has positioned its blueberry industry. “With trust, quality, and data-backed systems in place, India can command a premium in international markets. The future lies in collective, tech-led transformation,” she said. Keya’s remarks come at a time when India’s fresh produce exports, particularly grapes, pomegranates, and berries, are seeking to expand access to high-value markets across Europe, the Middle East, and East Asia.

“Agriculture has been passed down through generations as a traditional practice. But the next generation stepping into the sector is looking for something more. They want farming to be viable, technology-enabled, and market-smart. They want tools that assist with real-time decision-making, crop planning, price forecasting, and quality management. At the same time, their access to knowledge is often fragmented. The support systems—particularly for smallholders and MSMEs—need to be strengthened ” — Ajay Kakra, Leader, Food and Agriculture, Forvis Mazars

Artificial Intelligence in agriculture must shift from hype to real-world impact, said Ajay Kakra, Leader – Food and Agriculture at Forvis Mazars, calling for tech that is scalable, multilingual, and farmer-first. Speaking at the webinar, Kakra warned that while deep tech is surging ahead, it often overlooks the ground realities of Indian farming. “A farmer won’t adopt AI just because it’s available. It has to be simple, cost-effective, and deliver real value,” he said. He stressed the need for predictive decision-support tools—particularly for smallholders—to manage risks related to weather, pests, and price volatility. “AI must evolve from data science to practical systems that reduce uncertainty and support profitability,” he added.

Highlighting the need for better quality assessment, Kakra pointed to AI-led imaging tools and inventory tracking systems that can bring consistency and automation to grading, sorting, and export compliance. He also made a strong pitch for voice-based, local-language AI interfaces, open data ecosystems, and farmer data protection. “We need to move from screen to speech. From closed tech to open APIs. From pilots to scalable platforms,” he said. Kakra’s message was clear: if AI is to truly transform Indian agriculture, it must be built with empathy, trust, and a deep understanding of the people who grow our food.

Artificial Intelligence in agriculture must evolve from buzzword to backbone—serving as a scalable, decision-support system grounded in science, data, and field realities, opined Ravikant Bhargava, Associate Director at Cropin. “AI is only as good as the data it learns from. In agriculture, that means combining structured and unstructured inputs—crop models, soil data, remote sensing, and field-level practices—to generate real-time, actionable insights,” Bhargava said at the webinar. He stressed that crop monitoring isn’t just about health snapshots but about understanding inter-field variability, stage-specific needs, and predictive interventions. “Throwing a deep learning model at the problem won’t solve it. You need a full-stack intelligence layer, not a black box,” he noted.

“In a region in China, we were analysing GDD—growing degree days—to assess climate impact on crop suitability. Common logic would suggest that rising temperatures would damage crop prospects. But the data showed otherwise. The warming trend shifted certain regions from unsuitable to suitable, because the specific crop in question benefited from the added heat units. That’s the kind of counterintuitive insight you get when you separate fact from emotion, and let the science speak ” — Ravikant Bhargava, Associate Director, Cropin

Bhargava also emphasised the role of satellite data and climate modelling in scaling agri-AI for governments and large agri-businesses. From estimating paddy coverage in Sri Lanka to identifying heat-stressed zones in central India, AI must operate across both micro and macro layers, he said. In one case, climate models showed that rising temperatures in parts of China had unexpectedly improved crop suitability. “That’s what science does—it challenges intuition and replaces it with evidence,” he said.

For Bhargava, the message is clear: “AI isn’t magic. But when layered with agronomy, climate intelligence, and local context, it can answer big questions—what to grow, where to grow, and how to grow smarter.”
The session closed on a powerful, unanimous note: AI won’t fix agriculture alone, but it can transform everything if the ecosystem moves in sync.

Bhargava made the case for AI that builds trust, linking better food quality to smarter, sustainable practices. Shinde called out the need for a farmer-facing super-app that fuses agronomy, markets, and finance. Ramachandran demystified how edge computing and smart models can tame chaotic agri-data. Kakra pulled no punches on policy gaps, insisting that digitisation and geotagging must become mission-critical, not optional, while Keya painted a future of blockchain-backed, voice-enabled supply chains, while warning that context, not code, will be king.

Drones, sensors, AI pipelines—these are no longer the future; they’re the fuel. However, if startups, governments, and institutions don’t co-build, co-invest, and co-own this transformation, India risks watching the agri-AI revolution from the sidelines.

———– Suchetana Choudhury ( suchetana.choudhuri@agrospectrumindia.com )