Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning
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 suppo...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | http://dx.doi.org/10.1155/2024/5551759 |
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| author | Umar Farooq Shafi Waheed Anwar Imran Sarwar Bajwa Hina Sattar Iqra Yaqoob Aqsa Mahmood Shabana Ramzan |
| author_facet | Umar Farooq Shafi Waheed Anwar Imran Sarwar Bajwa Hina Sattar Iqra Yaqoob Aqsa Mahmood Shabana Ramzan |
| author_sort | Umar Farooq Shafi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cfe765aebcac41b486177426313fbb05 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-cfe765aebcac41b486177426313fbb052025-02-03T07:23:42ZengWileyInternational Journal of Distributed Sensor Networks1550-14772024-01-01202410.1155/2024/5551759Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine LearningUmar Farooq Shafi0Waheed Anwar1Imran Sarwar Bajwa2Hina Sattar3Iqra Yaqoob4Aqsa Mahmood5Shabana Ramzan6Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer Science & ITDepartment of Computer ScienceDepartment of Computer Science & ITDepartment of Computer Science & ITThe 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.http://dx.doi.org/10.1155/2024/5551759 |
| spellingShingle | Umar Farooq Shafi Waheed Anwar Imran Sarwar Bajwa Hina Sattar Iqra Yaqoob Aqsa Mahmood Shabana Ramzan Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning International Journal of Distributed Sensor Networks |
| title | Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning |
| title_full | Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning |
| title_fullStr | Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning |
| title_full_unstemmed | Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning |
| title_short | Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning |
| title_sort | smart predictor for spontaneous combustion in cotton storages using wireless sensor network and machine learning |
| url | http://dx.doi.org/10.1155/2024/5551759 |
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