AnaajSurakha: Detection of Corruption in GrainScan Supply Chain

Abstract

The grain supply chain provides food for people reliant on the government’s distribution systems; however, during the collection, storage, and delivery process issues including grain adulteration, loss of quantity, and alteration of records can be encountered due to the scale of the operation and the use of manual inspections. To meet these challenges, AnaajSuraksha is a practically developed system that detects corruption and irregularities in an early stage. The system utilizes GrainScan to take high-quality images of the grain and to weigh it using simple image processing techniques that will identify color changes, impurities, damage, or variations in the quantity of grain being measured. All of the records of the grain measurements are secured to prevent alterations and updates are made in real time to the administrators to provide transparency. By combining the systematic inspection, secure record-keeping and the efficient development of the system, AnaajSuraksha will improve food security and minimize disruption to the grain supply chain.

Introduction

The document presents AnaajSuraksha, a smart system designed to improve transparency and reduce corruption in the food grain distribution supply chain. The current ration distribution system faces issues such as grain adulteration, quantity reduction, poor recordkeeping, and manual errors, which lead to financial losses and reduced food quality.

To solve these problems, the proposed system uses a combination of GrainScan image processing, weight verification, and digital record management. Grain images are captured at different stages (procurement, storage, transport, and distribution) to detect impurities, color changes, texture differences, and foreign particles. Alongside this, a digital weighing system ensures that grain quantity matches expected values.

All data—including images, weight, timestamps, and operator details—is securely stored in a central database. An anomaly detection mechanism compares real-time data with standard values to identify possible corruption, adulteration, or quantity mismatches.

The system is supported by a hardware setup (camera, digital scale, processing unit, and connectivity modules) and software modules for image analysis, weight verification, and database management.

Experimental results show that the system can effectively detect:

• Adulterated grains (stones, dust, mixed grains)
• Quality issues (moisture, discoloration)
• Quantity fraud (underweight batches)

By combining image analysis and weight monitoring, the system improves accuracy and reduces the need for manual inspection.

Conclusion

TheAnaajSuraksha presents a simple yet reliable solution to reduce corruption in the grain supply chain. The system combines GrainScan-based image inspection with weight verifica- tion to detect issues such as adulteration, damaged grains, and quantity mismatches at an early stage. Instead of depending only on manual inspection, this approach adds an extra layer of verification that improves transparency. All transaction details, including grain images and weight records, are securely stored in the database, which minimizes the risk of record manipulation. During testing, the system showed consistent performance and was easy to operate, even for users with basic technical knowledge. The results indicate that integrating basic image analysis, structured recordkeeping, and automated verification can significantly improve monitoring in real supply chain environment.Overall,AnaajSurakshaenhances accountability, reduces human error, and supports fair and efficient grain distribution..

References

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Copyright

Copyright © 2026 Darshana Chudaman Gaikwad, Harshali Dipak Bhamare, Shruti Rajendra Khonde, Jagruti Vijay Deore, Sivaram Ponnusamy, Umesh Pawar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.