Agrokaari provides an AI‑powered platform that combines computer‑vision analysis with IoT sensor data to automatically grade produce and predict batch‑level shelf life for fruits and vegetables. The system delivers real‑time freshness scores, spoilage risk alerts, and integrates via secure REST APIs with ERP, WMS and retail inventory systems.
Funding
Funding not disclosed
Founders
Product
Problem
Post‑harvest loss of fruits and vegetables remains high because spoilage detection relies on manual visual inspection, which is slow, inconsistent, and often performed after significant quality degradation. Limited access to real‑time quality data hampers logistics planning and increases waste across the supply chain.
Solution
Agrokaari delivers an AI‑driven post‑harvest management platform that combines computer‑vision analysis with IoT sensor streams to evaluate produce quality at scale. High‑resolution images captured by mobile or fixed cameras are processed by deep‑learning models that identify defects, discoloration, and early spoilage. Concurrently, temperature, humidity, and ethylene sensors feed environmental parameters into a predictive analytics engine that forecasts remaining shelf life for each batch. The system automatically assigns standardized grade codes based on size, color, and defect severity, eliminating manual sorting. All results are synchronized to a cloud‑hosted dashboard where stakeholders receive real‑time freshness scores, trend visualizations, and actionable alerts. The platform exposes RESTful APIs for seamless integration with existing ERP, warehouse‑management, or retail systems, enabling data‑driven decisions throughout the distribution network.
Target Audience
Primary users are commercial growers, pack‑house processors, and distribution or retail operators that require automated quality inspection, predictive freshness analytics, and integration with supply‑chain management systems.
Features
- Deep‑learning computer‑vision pipeline that detects surface defects, bruising, and microbial spoilage with >95% accuracy.
- IoT sensor fusion (temperature, humidity, ethylene) to enrich image data and improve shelf‑life predictions.
- Predictive model that outputs batch‑level freshness windows and spoilage risk scores.
- Automated grading algorithm that assigns industry‑standard quality categories (e.g., U.S. No. 1, No. 2) based on multi‑dimensional criteria.
- Cloud analytics service with real‑time dashboards, trend graphs, and configurable alert thresholds.
- Secure REST API (OAuth 2.0) for integration with ERP, WMS, and retail inventory systems.
- End‑to‑end data encryption and compliance with ISO 27001 and GDPR privacy standards.