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Paper ID:IJSREM65292

Fake Fertilizer Detection and Authentication: A QR-Code Based Secure Verification Platform

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65292

Volume:

10 |

6 |

Jun 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65292

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

BBB

Balaji Baliram Bharati

Department of Computer Engineering

Sinhgad Institute of Technology & Science

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GIa

Gufran Imam ansari

Department of Computer Engineering

Sinhgad Institute of Technology & Science

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SGA

Sudarshan Gokul Ahire

Department of Computer Engineering

Sinhgad Institute of Technology & Science

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SSB

Siddhesh Sandip Beloshe

Department of Computer Engineering

Sinhgad Institute of Technology & Science

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Mentors

PKS

Prof. K.A. Shinde

Department of Computer Engineering

Sinhgad Institute of Technology & Science

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Abstract

The increasing circulation of counterfeit and adulterated fertilizers in the agricultural market poses a serious threat to crop productivity, soil health, and the financial stability of farmers. This paper presents a secure, technology-driven Fake Fertilizer Detection and Authentication System (FFIDAS) that leverages QR code-based verification and SHA-256 cryptographic hashing to ensure fertilizer authenticity across the supply chain. The proposed system integrates a Flutter-based mobile application for real-time QR scanning, a Node.js RESTful backend for server-side validation, a React.js web dashboard for administrative oversight, and a MongoDB Atlas cloud database for secure, scalable data storage. Fertilizer manufacturers register genuine product batches, generating unique QR codes embedded with cryptographic hashes. Farmers scan these codes to instantly verify product authenticity. Experimental evaluation demonstrates a QR verification accuracy of 98.4%, authentication success rate of 97.2%, and average verification latency of 1.8 seconds, with 91% user satisfaction. The system enhances transparency, reduces fraudulent distribution, and contributes to sustainable agricultural practices.

KEYWORDS

Fertilizer Authentication QR Code Verification SHA-256 Cryptography Supply Chain Security Mobile Application MongoDB React.js Flutter Node.js Counterfeit Detection.

Cite This Article

Balaji Baliram Bharati, Gufran Imam ansari, Sudarshan Gokul Ahire, Siddhesh Sandip Beloshe, Prof. K.A. Shinde, "Fake Fertilizer Detection and Authentication: A QR-Code Based Secure Verification Platform," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-7, Jun 2026. https://doi.org/10.55041/IJSREM65292

Full Article/Conclusion

This paper presented the Fake Fertilizer Detection and Authentication System (FFIDAS), a comprehensive technology-driven  platform  addressing  the  critical problem of counterfeit fertilizer circulation in agricultural markets. By integrating SHA-256 cryptographic hashing for tamper-proof QR code generation, a Flutter-based mobile application for real-time farmer-side verification, a React.js administrative dashboard for government oversight, a Node.js RESTful backend for secure business logic execution, and MongoDB Atlas for scalable cloud storage, FFIDAS delivers a complete multi-stakeholder authentication ecosystem.   Key contributions include: (1) a two-phase cryptographic authentication protocol ensuring deterministic and tamper-evident fertilizer verification, (2) a cross-platform mobile application enabling instant QR-based verification accessible to farmers on low-cost devices, (3) a role-based web dashboard supporting manufacturer registration, government approval, and analytics monitoring, (4) a scalable microservices backend with JWT-based security and AES-256 encrypted data storage, and (5) experimental validation demonstrating 98.4% verification accuracy, 97.2% authentication success rate, 1.8-second average latency, and 91% user satisfaction.   Future work will focus on offline verification capabilities, blockchain-based decentralized record management, AI-driven anomaly detection, and direct integration with national agricultural regulatory databases. FFIDAS represents a significant step toward transparent, trustworthy, and scalable fertilizer supply chain authentication, with direct benefits for farmer safety, agricultural productivity, and regulatory oversight.

References

[1]  M. Kini and K. Pai, “A Survey on QR-Based Product Authentication Techniques for Agricultural Supply Chains,” Int. J. of Computer Science and Engineering, vol. 6, no. 3, pp. 45–52, 2018. [2]  S. E. F. de Avila, A. da Luz Jr., A. de A. Araujo, and M. Cord, “An Approach for Automatic Fertilizer Batch Verification and QR-Based Evaluation,” in Proc. IEEE Conf. on Agricultural Technology and Smart Systems, IEEE, 2023, pp. 112–119. [3]   P. S. Jadhav and D. S. Jadhav, “QR Authentication Using Enhanced Code Pattern Analysis for Fertilizer Validation (QRAFPV),” Int. J. of Advanced Research in Information Technology, vol. 9, no. 2, pp. 78–86, 2023. [4]  T. Yao, T. Mei, and Y. Rui, “Detection of Authentic Fertilizer Packaging Using Deep Ranking and QR Verification,” in Proc. IEEE Conf. on Multimedia and Agricultural Security (ICMAS), IEEE, 2022, pp. 201–207. [5]      A. Kumar and R. Singh, “Blockchain-Based Traceability Framework for Fertilizer Distribution in Agricultural Supply Chains,” J. of Agricultural Informatics, vol. 13, no. 1, pp. 34–48, 2022. [6]  R. Patel, S. Mehta, and A. Sharma, “Mobile QR Code Authentication System for Agricultural Product Verification Using RESTful APIs and MongoDB,” in Proc. Int. Conf. on Advanced Computing and Intelligent Engineering (ICACIE), Springer, 2021, pp. 145–155. [7]   C. Wu and S. Wu, “Embedding Authentic Batch Details in QR Codes for Fertilizer Verification Using Data-Hiding Techniques,” IEEE Access, vol. 8, pp. 142301–142315, 2020. [8]   M. Rafsanjani, H. Karimi, and B. Ahmadi, “Secure Mobile QR Scanning Framework for Preventing Fake Agricultural Products Using Tamper-Proof Verification,” Computers and Electronics in Agriculture, vol. 207, p. 107740, 2023. [9]  N. Mehrabi, F. Mors tatter, N. Saxena, K. Lerman, and A. Galstyan, “A Survey on Bias and Fairness in Machine Learning,” ACM Computing Surveys, vol. 55, no. 6, pp. 1–38, 2023. [10]   M. Singh and P. Patel, “Deepfake Detection and Liveness Verification for Secure Online Product Authentication,” in Proc. IEEE Conf. on Multimedia and Security (ICMS), IEEE, 2023, pp. 421–427.

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Publication Date

6/23/2026

Peer Review Type

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Review Rounds: 2

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