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
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