UGC Approved Journal no 63975(19)
New UGC Peer-Reviewed Rules

ISSN: 2349-5162 | ESTD Year : 2014
Volume 13 | Issue 9 | September 2026

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Volume 13 Issue 9
September-2026
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2609077


Registration ID:
585779

Page Number

a631-a639

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Title

AI Enabled Iot-Based Smart Food Grain Warehouse Monitoring And Predictive Protection System Using Machine Learning

Abstract

Food grain storage is essential for maintaining food quality and reducing post-harvest losses. This paper presents an AI-Enabled IoT-Based Smart Food Grain Warehouse Monitoring and Predictive Protection System for continuous and intelligent warehouse monitoring. The system uses an ESP32 microcontroller with a DHT11 temperature–humidity sensor, an MQ-135 air-quality sensor, a grain-moisture sensor, an LDR, a flame sensor, a magnetic door sensor and an IR sensor to monitor temperature, humidity, grain moisture, air quality, fire, door status, movement and light conditions. The collected data are transmitted over Wi-Fi to the ThingSpeak cloud platform for real-time logging and visualization. A Random Forest machine-learning model predicts grain spoilage risk, fungal growth risk, storage condition and Remaining Storage Life (RSL), while a Decision Tree-based AI model classifies the overall warehouse condition as Normal, Warning or Critical and generates preventive recommendations. A Warehouse Health Score (WHS), Smart Grain Health Index (SGHI) and Fungal Growth Risk Index (FGRI) are computed and displayed on a Streamlit dashboard with Plotly graphs. Based on the detected condition the system automatically operates an exhaust fan and water pump and raises buzzer/GSM alerts. The system was validated with data collected from an actual warehouse; the latest observation (30 °C, 55% RH, 157 ppm MQ-135) produced a WHS of 86.5/100 and a Normal classification that agreed with the field observation, giving 100% agreement over the available field observations. The proposed system thus integrates IoT, cloud computing, Random Forest ML and Decision Tree AI into a single predictive, user-friendly solution for improving food-grain warehouse safety and minimizing manual monitoring.

Key Words

Internet of Things (IoT), ESP32, ThingSpeak, Random Forest, Decision Tree, Food Grain Warehouse, Predictive Protection, Warehouse Health Score, Remaining Storage Life.

Cite This Article

"AI Enabled Iot-Based Smart Food Grain Warehouse Monitoring And Predictive Protection System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.a631-a639, September-2026, Available :http://www.jetir.org/papers/JETIR2609077.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"AI Enabled Iot-Based Smart Food Grain Warehouse Monitoring And Predictive Protection System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppa631-a639, September-2026, Available at : http://www.jetir.org/papers/JETIR2609077.pdf

Publication Details

Published Paper ID: JETIR2609077
Registration ID: 585779
Published In: Volume 13 | Issue 9 | Year September-2026
DOI (Digital Object Identifier):
Page No: a631-a639
Country: COIMBATORE, TAMILNADU, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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