Adaptive AI-Based Grading and Sorting for Quality Assessment in Horticulture Crops

India produces over 100 million tonnes of fruits annually and ranks among the world's top producers. Despite this, nearly 40% of fruit—valued at approximately 26 billion INR—is lost each year due to inefficiencies in harvesting, sorting, grading, and post-harvest handling. Manual grading dominates most packhouses and mandis, making quality assessment subjective, slow, and dependent on skilled labour. Even a single defective fruit during auction can reduce prices for entire consignments, disproportionately affecting farmers. Orchard labour requirements are nearly nine times higher than cereal crops, creating a strong need for a scalable, adaptive, and transparent AI-driven solution.

The solution is an AI-powered computer vision system embedded within indigenous optical grading and sorting machines for high-value fruits including kinnow, orange, apple, and pomegranate. High-speed cameras, controlled illumination, and deep learning models analyse fruits in real time for colour, size, shape, and surface defects such as blemishes, pest damage, cracks, and fungal marks. Unlike rule-based methods, the system continuously learns from diverse datasets collected across geographies, seasons, and fruit varieties, enabling accurate detection of region-specific defects and consistent performance under variable conditions. In addition to grading, the system generates quantitative defect profiles at the lot level, transforming grading from subjective judgment into data-backed quality assessment.

Deployment spans packhouses operated by farmers, FPOs, traders, and procurement companies across multiple Indian states. A human-in-the-loop framework allows supervisors to validate outputs and improve model performance through feedback. More than 90 AI-powered grading installations are active across India, processing thousands of tonnes annually and handling up to 200,000 kilograms per hour during peak harvest. The organisation has served over 0.3 million farmers through its broader mechanisation portfolio and a 200+ dealer network.

Field results show clear impact. Manual grading typically achieves 60–80% accuracy; AI improves this to over 95%. Processing speed has nearly tripled, labour costs have fallen by 40%, and post-harvest losses have declined up to 20%. Consistent grading reduces auction disputes and improves price realisation. At a system level, the technology reduces food waste and increases transparency across horticulture value chains. Ethical governance includes auditable AI decisions, human oversight, bias monitoring, and compliance with data protection norms. The deployment shows that AI delivers real value when embedded in practical hardware aligned with farmer economics, with early challenges around trust and dataset diversity addressed through transparency and visible financial gains for adopting farmers and packhouses.
For additional context and detailed documentation of this use case, please refer to pages 37-38 in the attached Casebook.