As agriculture embraces digital transformation, a key question arises — can technology bring fairness and consistency to how we define quality in food? Across supply chains, quality determines value but is still judged by the human eye, creating inefficiencies and waste. AI and data-driven tools are changing that by making quality measurable and transparent. In this evolving agrotech landscape, Intello Labs uses AI and computer vision to digitize quality assessment in fruits and vegetables, turning subjective judgments into objective data. The result — greater trust, fairer pricing, and a more transparent value chain from farm to market.
Leading this conversation is Milan Sharma, Co-founder and CEO of Intello Labs, who brings both a technical and systems-level understanding of how digitalization can reshape agricultural value chains. His perspective goes beyond technology as an enabler — toward how AI-driven insights can influence transparency, improve livelihoods, and create new definitions of value in agriculture. In this interview, he reflects on the evolving role of agrotech in transforming post-harvest systems, the lessons learned in scaling AI across diverse farm ecosystems, and the need to balance automation with human expertise in the future of farming.
Intello Labs began with the question, “Can we make quality smarter?” What inspired this idea, and what key challenges in agricultural supply chains were you aiming to solve when you started?
Milan Sharma: When we started Intello Labs, our belief was simple: quality determines value, yet in agriculture, quality was judged by the human eye. This created inconsistencies, inefficiencies, and mistrust across the chain. Farmers were underpaid, buyers over-hedged, and huge losses occurred in between. We wanted to bring objectivity into this process — to turn quality into a measurable, data-driven parameter that could power fair pricing and better planning across the food supply chain.
How does Intello Labs apply AI, machine learning, and computer vision to transform the way agro-commodities — especially fruits and vegetables — are graded and valued across the supply chain?
Milan Sharma: We use advanced computer vision and deep-learning models to see and interpret produce the way a human expert would — but with the precision of a machine. Our systems analyze attributes like size, color, surface defects, and ripeness within seconds, grading each fruit or vegetable against objective benchmarks.
This data then drives digital quality scores and enables buyers, exporters, and retailers to make procurement and pricing decisions in real time — creating a common language of quality from farm to fork.
What were the major technical and operational challenges in training AI models to evaluate crop quality accurately across different varieties, regions, and post-harvest conditions?
Milan Sharma: Agriculture is beautifully complex — no two apples or tomatoes are ever identical. The biggest challenge was variability — across geographies, lighting conditions, and camera hardware.
We built one of the world’s largest labeled datasets of fruits and vegetables, spanning multiple seasons and regions, and used adaptive AI models that learn from new environments continuously.
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Operationally, ensuring consistency in image capture at packhouses and farms was equally critical — so we designed modular systems and strict calibration protocols to ensure our models “see” the same way everywhere.
Manual grading has long shaped how agricultural produce is traded. How does Intello Labs drive trust and adoption of digital quality assessment among farmers, aggregators, and exporters?
Milan Sharma: Trust is earned through transparency. We ensure that every grading decision is backed by visual evidence and standardized digital reports. Farmers see why their lot was graded the way it was; buyers see the same data.
We also work closely with farmer cooperatives, exporters, and packhouse operators to align digital grading results with market expectations. Over time, this consistency builds confidence — and farmers begin to realize that quality differentiation leads to real price differentiation.
Food loss and waste are critical global agrotech challenges. How severe is this issue in India and globally, and what measurable reductions has Intello Labs achieved through its technology?
Milan Sharma: Globally, nearly 40% of fruits and vegetables are lost before reaching consumers — in India, the number is often higher. Most of this happens post-harvest, due to poor grading, handling, and storage.
By digitizing quality and streamlining segregation at source, Intello Labs helps reduce rejection and wastage by 15–25% across packhouses and distribution centers. That translates into higher income for farmers, better yield utilization for buyers, and a smaller environmental footprint overall.
Could you share a case study where Intello Labs’ solution directly improved supply chain efficiency, ensured fairer pricing, or significantly reduced post-harvest wastage?
Milan Sharma: A great example is our work in Digital Mandi for Apples with Adani Agri Fresh in Himachal Pradesh. Traditionally, apple trading was manual and opaque. By digitizing grading and auctioning, farmers now receive 10–15% higher prices, and buyers get consistent quality and faster turnaround.
The platform processes thousands of crates daily, with every transaction traceable from orchard to buyer — a complete transformation of how value is created and shared in the ecosystem.
By digitizing quality assessment, Intello Labs is also influencing how agricultural value is defined. How does this help create more transparency and fairness for farmers and buyers alike?
Milan Sharma: When quality becomes data, value becomes transparent. Our systems remove subjectivity and create a single version of truth visible to all stakeholders.
This not only ensures farmers are rewarded for genuine quality but also allows buyers to plan procurement more accurately, minimize disputes, and optimize their supply chains. Essentially, digital quality builds trust equity in agriculture.
Your platform is used by growers, packers, exporters, and food-service companies. How do you adapt and scale your technology across such diverse agro stakeholders, and what role do collaborations or partnerships play in this journey?
Milan Sharma: Our technology stack is modular — the same AI engine powers handheld apps for farmers, grading lines for packhouses, and API integrations for enterprise buyers.
Partnerships are key. We collaborate with government agencies, exporters, large agro-corporates, and startups globally. Each collaboration helps refine our models for new geographies and commodities, enabling scalability without losing local context.
Intello Labs envisions a transparent and data-driven agro supply chain. How do you see that vision evolving in the next phase — and are there plans to extend your technology beyond fruits and vegetables into other commodities?
Milan Sharma: The next phase is about interoperability — connecting grading data with traceability, logistics, and financing. Once quality data becomes a digital asset, it can unlock better credit, insurance, and demand planning.
We are already piloting extensions into grains, nuts, and spices, where similar challenges of subjectivity exist. Our goal is to make “digital quality” a universal standard across the agro-value chain.
As AI and digital technologies become integral to agriculture, how do you see the balance between automation and human expertise evolving — and what advice would you offer to innovators shaping the future of agrotech?
Milan Sharma: AI doesn’t replace human expertise — it amplifies it. The future lies in collaboration between technology and field wisdom. Our advice to agrotech innovators: stay grounded in the realities of the farmer. Technology must simplify, not complicate. The most powerful innovations in agriculture will be those that blend empathy, data, and scalability.