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The wastage of foodgrains due to improper storage conditions is a critical issue affecting the Indian agriculture sector. In India 10% of the total foodgrain is wasted every year. This work introduces GrainLog, an advanced solution designed to modernize grain storage by leveraging Internet of Things (IoT) and Machine Learning (ML) technologies. GrainLog enables remote monitoring of granaries through IoT sensors and provides insights via a user-friendly app, helping farmers make informed decisions. The system features a microcontroller-based sensor network for real-time grain condition monitoring, with data processed on a central cloud server. A method is proposed which utilizes ML algorithms trained on extensive datasets, for predicting grain shelf life and enhancing storage management. Its collaborative, crowdsourced data approach amplifies its utility, making it accessible and beneficial for a wide range of users, including those in rural areas. GrainLog aims to reduce food grain wastage, improve supply chain efficiency, and safeguard the economic interests of farmers and intermediaries.

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GrainLog: An IoT-ML Based Food Grain Monitoring and Shelf-Life Prediction System for the Farmer

  • 会议论文

  • 发表日期：

    2025年

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  • Vaidehi Sinha,
  • Chaitanya Kulkarni,
  • Ujjwala Rawandale,
  • Abhishek Hosur,
  • Rishabh Humbare,
  • Trushita Chaware

摘要

The wastage of foodgrains due to improper storage conditions is a critical issue affecting the Indian agriculture sector. In India 10% of the total foodgrain is wasted every year. This work introduces GrainLog, an advanced solution designed to modernize grain storage by leveraging Internet of Things (IoT) and Machine Learning (ML) technologies. GrainLog enables remote monitoring of granaries through IoT sensors and provides insights via a user-friendly app, helping farmers make informed decisions. The system features a microcontroller-based sensor network for real-time grain condition monitoring, with data processed on a central cloud server. A method is proposed which utilizes ML algorithms trained on extensive datasets, for predicting grain shelf life and enhancing storage management. Its collaborative, crowdsourced data approach amplifies its utility, making it accessible and beneficial for a wide range of users, including those in rural areas. GrainLog aims to reduce food grain wastage, improve supply chain efficiency, and safeguard the economic interests of farmers and intermediaries.