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Design and Development of a Smart Silo Grain Storage Monitoring System

 

 

Ragavi VJ1 and Dr A Reni2*

1Post Graduate, Department of Food processing and preservation technology, School of Engineering

2*Associate Professor, Department of Food processing and preservation technology, School of Engineering

Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India

Email: 24pef003@avinuty.ac.in

 

 

Abstract

Smart silo grain storage systems combine Internet-of-Things (IoT) sensor networks, targeted biosensing, and data-driven analytics to provide continuous, in-situ monitoring and early warning of spoilage risks in stored cereals and pulses. Recent IoT implementations demonstrate low-cost, real-time monitoring of temperature, relative humidity, and gas concentrations (e.g.,CO₂) to detect conditions that precede insect infestation and fungal growth, enabling automated aeration and stakeholder alerts. Biosensor research, particularly aptamer, immuno and nanomaterial enhanced electrochemical/ optical sensors, has advanced point-of-care detection of key mycotoxins (notably aflatoxin B₁), providing sensitive, rapid, and portable tools that can complement environmental sensing for safety verification. Integration of biosensing with silo-level IoT is emerging as a feasible strategy to couple hazard detection (mycotoxins/pathogens) with environmental triggers, reducing reliance on slow laboratory assays. Meanwhile, AI and machine-learning models (from anomaly-detection frameworks to deep-learning fusion models) have been applied to multi-sensor time series to predict spoilage events and optimize control actions, improving early detection accuracy and reducing false alarms. Deploying these hybrid systems is especially relevant in high-loss contexts such as India, where post-harvest and storage losses remain economically significant; targeted smart monitoring can materially reduce quantitative and qualitative losses when combined with appropriate maintenance and connectivity strategies. Key challenges remain sensor calibration and drift, cost and power constraints in rural deployments, data security, and end-user adoption, but the convergence of IoT, biosensors, and AI offers a scalable pathway to safer, lower-loss grain storage.

Keywords: Smart silo; IoT grain monitoring; biosensors; mycotoxin detection; aflatoxin B₁; anomaly detection; post-harvest losses; real-time monitoring; stored grain safety.

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