Research on Fire Detection of Cotton Picker Based on Improved Algorithm
According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefo...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/564 |
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| author | Zhai Shi Fangwei Wu Changjie Han Dongdong Song |
| author_facet | Zhai Shi Fangwei Wu Changjie Han Dongdong Song |
| author_sort | Zhai Shi |
| collection | DOAJ |
| description | According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers. |
| format | Article |
| id | doaj-art-d1061987b11c440e92b7e88186ba416d |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d1061987b11c440e92b7e88186ba416d2025-01-24T13:49:22ZengMDPI AGSensors1424-82202025-01-0125256410.3390/s25020564Research on Fire Detection of Cotton Picker Based on Improved AlgorithmZhai Shi0Fangwei Wu1Changjie Han2Dongdong Song3College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaCollege of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, ChinaAccording to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers.https://www.mdpi.com/1424-8220/25/2/564fusion algorithmneural networkcotton pickersfire detection system |
| spellingShingle | Zhai Shi Fangwei Wu Changjie Han Dongdong Song Research on Fire Detection of Cotton Picker Based on Improved Algorithm Sensors fusion algorithm neural network cotton pickers fire detection system |
| title | Research on Fire Detection of Cotton Picker Based on Improved Algorithm |
| title_full | Research on Fire Detection of Cotton Picker Based on Improved Algorithm |
| title_fullStr | Research on Fire Detection of Cotton Picker Based on Improved Algorithm |
| title_full_unstemmed | Research on Fire Detection of Cotton Picker Based on Improved Algorithm |
| title_short | Research on Fire Detection of Cotton Picker Based on Improved Algorithm |
| title_sort | research on fire detection of cotton picker based on improved algorithm |
| topic | fusion algorithm neural network cotton pickers fire detection system |
| url | https://www.mdpi.com/1424-8220/25/2/564 |
| work_keys_str_mv | AT zhaishi researchonfiredetectionofcottonpickerbasedonimprovedalgorithm AT fangweiwu researchonfiredetectionofcottonpickerbasedonimprovedalgorithm AT changjiehan researchonfiredetectionofcottonpickerbasedonimprovedalgorithm AT dongdongsong researchonfiredetectionofcottonpickerbasedonimprovedalgorithm |