International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 2
March-April 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

DeepAgriScan: Intelligent Produce Recognition System for Smart Retail Billing

Author(s) Dr. Siva Krishna, Ms. Mounika Jakki, Ms. Jahnavi Muppalla, Mr. Bhanu Manohar Reddy Mekala, Mr. Devi Vara Prasad Bommanaboina, Dr. K. Siva Krishna
Country India
Abstract DeepAgriScan is an intelligent automated system designed to address the limitations of manual fruit identification and billing in modern retail environments. Fruits and vegetables, unlike packaged goods, do not carry barcodes, making automated identification inherently challenging. The proposed system leverages deep learning and computer vision to capture, process, and classify fruit images with high accuracy. A Convolutional Neural Network (CNN) model is trained on a diverse dataset of fruit images and is capable of recognizing fruits based on visual features such as shape, color, and texture. Upon successful identification, the system retrieves the corresponding price from a structured database and automatically generates the billing output, eliminating manual intervention entirely.

The pipeline integrates image preprocessing (resizing, normalization, augmentation), CNN-based feature extraction and classification, price retrieval, and bill generation into a unified end-to-end system. Implemented using Python and TensorFlow, the system achieves an average top-1 classification accuracy of 95.5% across six fruit categories. Experimental results demonstrate that DeepAgriScan significantly reduces checkout time and billing errors compared to traditional manual methods. The system is designed for scalability and can be extended to support additional produce categories, real-time video streams, and integration with weighing machines for weight-based pricing.
Keywords Deep Learning, Convolutional Neural Network (CNN), Computer Vision, Fruit Recognition, Image Processing, Smart Retail, Automated Billing, TensorFlow, Artificial Intelligence
Field Computer > Data / Information
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-29
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.72523
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 1Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals