Agrograde targets tomato and garlic AI grading as deployments top 130 machines

Pune-based Agrograde is developing AI-powered sorting and grading systems for tomatoes, garlic and leafy vegetables, extending beyond onions, potatoes, apples and arecanut. The company says its machines can lower grading costs, reduce labour needs and improve consistency for FPOs, exporters, aggregators and processors.

— Source publishedMon, 21 Sept, 2026, 09:30 IST·First seen Mon, 21 Sept, 2026, 09:35 IST·Source BL · Consumer & Economy

What happened

Pune-based Agrograde is developing AI sorting and grading systems for tomatoes, garlic and leafy vegetables, after deployments in onions, potatoes, apples and

Key facts

  • Launched in 2018
  • Over 130 machines deployed
  • Operations across 14 states and four crop value chains
  • Sorting and grading cost reduced from ₹0.40-₹1/kg to ₹0.08-₹0.10/kg
  • Around 99% size-grading accuracy
  • Machine capacity: about 10 tonnes/hour
  • 22 workers required versus 60-80 previously for a 200-tonne onion order
  • Machine prices range from ₹2.5 lakh to nearly ₹1 crore

Why this matters

Retailers, exporters and agri-supply-chain platforms may view AI grading providers as partnership or acquisition targets to strengthen quality assurance, procurement efficiency and traceability.

What to watch

  • Named deployments with national supermarket chains, quick-commerce firms, large FPOs or export packhouses.
  • Evidence that machines deliver measurable reductions in shrink, labour cost, buyer rejections or retailer return rates.
  • Financing, leasing or pay-per-kg grading models that lower adoption barriers for small aggregators.
  • Expansion into packhouse traceability, residue testing, digital auctions or demand forecasting, which would make grading part of a broader procurement stack.
  • Tomato and garlic price shocks, as extreme supply volatility may either accelerate loss-reduction investments or delay capex by cash-constrained suppliers.
  • Growth in secondary-market, processing or discount-retail channels for machine-rejected produce.
  • Organized grocers and quick-commerce fresh teams are likely to seek supply agreements with AI-enabled FPOs and aggregators for high-velocity tomato, garlic and leafy-vegetable SKUs.
  • Retailers may redesign produce buying around standardized grade bands, linking vendor scorecards to defect rates, shelf-life performance and fill rates rather than visual inspection alone.
  • Large suppliers may bundle grading machines with packhouse services, financing and traceability software to raise utilization and lock in retailer contracts.
  • Private-label processors could use more consistent incoming grades to reduce raw-material variability in sauces, pastes, dehydrated garlic and ready-to-cook vegetable products.
  • Retailers may expand lower-grade produce offers through value packs, food-service channels or processing partnerships to capture product that automated systems reject from premium shelves.

Also reported by