Fruit and vegetable self-billing system based on image recognition

Introduction: Shopping centers have become a necessary aspect of living, especially for city dwellers. To realize the identification and settlement of fruits and vegetables lacking bar codes is a major problem in supermarket self-service settlement. Methods: In this study, we proposed a novel Garra Rufa fish-optimized multi-objective convolutional neural network (GRFO-MCNN) for fruit and vegetable detection and freshness recognition. To improve feature identification performance, the GRFO-MCNN integrates the CBAM, which consists of the CAM and the SAM. Freshness recognition and fruit and vegetable detection are greatly enhanced by the CBAM by focusing on pertinent regions of images. Results: The proposed model integrate with the automated settlement system which transform the fruits and vegetable purchases by streamline identification and payment process. The Raspberry Pi, a microcontroller with a camera unit, makes up the suggested model to automate the billing system. For this study, we used a Raspberry Pi module to automatically acquire image data of fruits and vegetables. Conclusions: The suggested approach is contrasted with the other traditional approaches. The result shows the suggested approaches outperformed in accuracy (0.93), MAE (0.11), and RMSE (0.53). The fruits and vegetables that are arranged for automatic weighing are captured by the camera module. The microprocessor receives as an input the cost of various products per kilogram automatically. Consequently, the Raspberry Pi automatically calculates and shows the overall lprice of the products on the monitor