Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{196548,
        author = {Tejas D S and Vidyashree B P and B Preetham and Cheluvaraju Y and Hariprasad D R},
        title = {IoT-Based Grain Quality Monitoring System with Real-Time Environmental Analysis and Predictive Insights},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4561-4568},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196548},
        abstract = {Grain storage losses remain a critical challenge in developing countries due to inadequate monitoring of environmental conditions such as temperature, humidity, moisture content, and pest activity. Traditional storage practices rely on manual inspection and lack real-time visibility, leading to delayed interventions and significant post-harvest losses. This paper presents an IoT-based Grain Quality Monitoring System designed to provide continuous, real-time assessment of storage conditions using low-cost sensors and cloud connectivity. The proposed system integrates temperature, humidity, moisture, gas, and motion sensors with a microcontroller-based architecture to monitor grain storage environments effectively. Sensor data are transmitted wirelessly to a cloud platform for visualization, historical analysis, and alert generation. In addition, machine learning techniques are employed to predict grain shelf life and assist in grain-type identification, enabling proactive decision-making for improved storage management. The system is designed to be portable, affordable, and user-friendly, making it suitable for small-scale farmers and decentralized storage facilities. Experimental evaluation demonstrates reliable real-time monitoring, early detection of unfavorable storage conditions, and improved decision support through predictive analysis. The proposed solution offers a scalable and practical approach to reducing post-harvest grain losses and enhancing food security through intelligent storage monitoring.},
        keywords = {Internet of Things (IoT), Grain Quality Monitoring, Smart Agriculture, Sensor Networks, Machine Learning, Post-Harvest Management.},
        month = {April},
        }