Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning

Author:

Shafi Umar Farooq1ORCID,Anwar Waheed1ORCID,Bajwa Imran Sarwar1ORCID,Sattar Hina2ORCID,Yaqoob Iqra1ORCID,Mahmood Aqsa2ORCID,Ramzan Shabana2ORCID

Affiliation:

1. Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

2. Department of Computer Science & IT, Government Sadiq College Women University, Bahawalpur 63100, Pakistan

Abstract

The splendid technological inventions supersede many traditional agricultural monitoring systems. In the last decade, a variety of new techniques and tools are proposed to monitor storage areas, which provide more safe and secure storage for different crops. The term storage area monitoring is supposed to check and avoid fire hazards, whereas numerous other hazards also need attention. One such hazard to cotton storage is spontaneous combustion, a process by which an element having comparatively low ignition temperature (hay, straw, peat, etc.) starts to relieve heat. In the presence of spontaneous combustion and lack of oxygen, if cotton catches any sparks from bales or physicochemical heat to ignite, the combustion can convert in to smoldering, and it can last up to several days without being discovered. Consequently, the actual fire occurs, cotton silently smoldering which not only affects cotton quality but also became the reason of big fire event. Many researchers propose valuable tools and techniques based on laboratory methods and modern techniques as well for detection and prevention of security hazards in storages. However, there is no standalone efficient tool/technique to monitor the storage area for spontaneous combustion. In current research, we propose an efficient wireless sensor network (WSN) and machine learning- (ML-) based storage area monitoring system for early prediction of spontaneous combustion in the cotton storage area. The WSN is used to collect real-time values from storage field by different combinations of sensors and send this over the network, where data is processed to identify spontaneous combustion and distribute the prediction results to the end user. The real-time data collection and ML-based analysis make the system efficient and reliable. The efficiency of the current system is verified by presenting two groups of cotton stored with different conditions. The results showed that the proposed system is able to detect spontaneous combustion well in time with a 95% accuracy rate.

Funder

M. Nazam Group of Industries

Publisher

Hindawi Limited

Reference28 articles.

1. Climate change and cotton production: an empirical investigation of Pakistan

2. Fire risk assessment in cotton storage based on fuzzy comprehensive evaluation and Bayesian network

3. Study on Fire Risk and Disaster Reducing Factors of Cotton Logistics Warehouse Based on Event and Fault Tree Analysis

4. A note on self heating and spontaneous combustion of stored sunflower seed cake and cotton seeds;S. M. El-Nazir;University of Khartoum Journal of Agricultural Sciences,2012

5. Study on the characteristic comparative of cotton smoldering and flame combustion;E. L. Xia;Fire Safety Science,2013

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3