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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM68482

Vegetable detection and automated bill generation

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68482

Volume:

10 |

10 |

Oct 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Riddhi Wasalwar

Riddhi Wasalwar

Department of information technology

Priyadarshini college of engineering, Nagpur

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Divya Chaware

Divya Chaware

Department of information technology

Priyadarshini college of engineering, Nagpur

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Tushar Belge

Tushar Belge

Department of information technology

Priyadarshini college of engineering, Nagpur

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Ritesh Agrawal

Ritesh Agrawal

Department of information technology

Priyadarshini college of engineering, Nagpur

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PVNC

Prof. Vicky N. Chaudhari

Department of information technology

Priyadarshini college of engineering, Nagpur

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PSGM

Prof. Snehal G. Motghare

Department of information technology

Priyadarshini college of engineering, Nagpur

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Bhakti Thaware

Bhakti Thaware

Department of information technology

Priyadarshini college of engineering, Nagpur

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Abstract

The Automated Vegetable Detection and Billing System is an intelligent retail solution designed to automate vegetable identification, weight measurement, price calculation, and bill generation using artificial intelligence and embedded system technology. The proposed system uses a camera module to capture images of vegetables and the YOLOv8 object detection algorithm to identify and classify different vegetable categories. A load cell connected to an HX711 amplifier measures the weight of vegetables, while the ESP32 microcontroller coordinates sensor readings and billing operations. Based on the detected vegetable category, measured weight, and predefined price per kilogram, the system calculates the total cost and displays billing details on an LCD or OLED screen. This approach reduces manual effort, minimizes billing errors, and improves checkout efficiency in supermarkets, grocery stores, and vegetable retail shops. The system can be further enhanced with digital payments, automatic receipt generation, and inventory management to support smart retail automation.

KEYWORDS

Automated Vegetable Detection YOLOv8 Deep Learning Computer Vision ESP32 Load Cell HX711 Auto-matic Billing Smart Retail

Cite This Article

Riddhi Wasalwar, Divya Chaware, Tushar Belge, Ritesh Agrawal, Prof. Vicky N. Chaudhari, Prof. Snehal G. Motghare, Bhakti Thaware, "Vegetable detection and automated bill generation," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-5, Oct 2026.

Full Article/Conclusion

The Automated Vegetable Detection and Billing System integrates artificial intelligence, computer vision, and embed-ded system technology to simplify vegetable retail billing operations. By combining YOLOv8-based vegetable recognition with load cell-based weight measurement and automatic price cal-culation, the system offers a convenient alternative to tradi-tional manual billing methods. The ESP32 manages sensor interfacing and display opera-tions, while a connected computer or suitable edge-processing device executes the vegetable detection model. The system is designed to improve billing speed, reduce hu-man errors, and provide clear billing information to customers and shopkeepers. With further improvements in detection accuracy, sensor calibration, and system integration, the system can be en-hanced with digital payments, receipt generation, inventory management, and cloud-based transaction records. These improvements can make the proposed system a promising solution for smart retail applications.

References

[1]     S. Kim, H. Lee, and J. Park, “Smart Lighting Integration for Au-tonomous Checkout Systems,” IEEE International Symposium on Smart Technologies, pp. 75–82, 2022. [2]     “Design of an Intelligent Shopping Cart Based on STM32F40TVET6,” 2023 13th International Conference on Information Technology in Medicine and Education (ITME), 2023. [3]     A. Titus, M. U. S, N. Krishnan G, and M. Francis, “Deep Weight: A Semi-Autonomous Food Weight Billing System using Deep Learning,” 2023 International Conference on Control, Communication and Com-puting (ICCC), pp. 1–6, 2023. [4]     M. R. Barbosa Junior, R. G. Santos, L. A. Sales, and L. P. Oliveira, “Advancements in agricultural ground robots for specialty crops: an overview of innovations, challenges, and prospects,” Plants, vol. 13, pp. 1–20, 2024. [5]     J. Zhang, N. Kang, Q. Qu, L. Zhou, and H. Zhang, “Automatic fruit picking technology: A comprehensive review of research advances,” Artificial Intelligence Review, vol. 57, pp. 1–35, 2024. [6]     E. Nandhini and V. G., “A comprehensive analysis of image processing methods for agricultural product quality control,” 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), pp. 1–7, 2024. [7]     R. Lalam, K. Lavanya, V. Nadella, and B. R. Kiran, “Automatic sorting and grading of fruits based on maturity and size using machine vision and artificial intelligence,” Journal of Scientific Research and Reports, vol. 37, no. 3, pp. 1–12, 2025. [8]     K. Singh, J. Singh, and R. Rani, “Computer vision approach for vegetable quality assessment,” 2025 7th International Conference on Intelligent Sustainable Systems (ICISS), pp. 1–5, 2025. [9]     “Evaluating agricultural production line efficiency using TOPSIS: A comparative analysis of crops and processing stages,” Agricultural Biology and Food Science, vol. 5, no. 1, pp. 1–10, 2025. [10]    P. Majumdar and V. Jain, “A Data-Driven Decision Support Framework for Crop Recommendation Based on Soil and Climatic Factors,” Sus-tainable Global Societies Initiative, vol. 1, no. 1, 2026.

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Publication Date

10/29/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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