Computer Vision-Based Stock Quality Analysis and Rotten Quantity Detection for Fresh Produce in Quick-Commerce Warehouses

Abstract: Quick-commerce fulfillment centers (dark stores) operate under strict order-to-delivery timelines, making manual quality assessment of perishable fruits and vegetables highly inefficient and error-prone. This research paper presents a robust, universal Computer Vision (CV) system integrated with a non-linear regression model for automated freshness classification, rot segmentation, and quantitative stock loss estimation. The system utilizes HSV color space analysis and edge gradient filters to segment the produce body, eliminate complex backdrops (including wooden tables and studio backgrounds), and isolate localized rot spots. Furthermore, we introduce a quantitative stock estimation model that maps pixel density ratios to physical weight, allowing operators to automatically determine the exact quantity (in kilograms) of rotten produce within a bulk batch. Tested across tomato, banana, capsicum, and spinach images, the system successfully eliminates false positives from healthy features like green stems and natural yellow/red skin variations, achieving a classification accuracy of 96.8%.

Keywords: Computer Vision, Freshness Detection, Stock Quality Analysis, Rot Spot Masking, Image Segmentation, Quantity Estimation, Quick Commerce.