{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T18:51:51Z","timestamp":1760986311908,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,19]],"date-time":"2025-01-19T00:00:00Z","timestamp":1737244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xinjiang Uygur Autonomous Region Natural Science Foundation Project \u2018Research on Fire Mechanism and Detection System of Cotton Picker\u2019","award":["2022D01A77","YTHSD 2022-08"],"award-info":[{"award-number":["2022D01A77","YTHSD 2022-08"]}]},{"name":"Xinjiang agricultural machinery R &amp; D and manufacturing promotion and application integration project \u2018Cotton picker automatic alignment navigation and fire detection system R &amp; D and manufacturing promotion and application\u2019","award":["2022D01A77","YTHSD 2022-08"],"award-info":[{"award-number":["2022D01A77","YTHSD 2022-08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s25020564","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T10:32:15Z","timestamp":1737369135000},"page":"564","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on Fire Detection of Cotton Picker Based on Improved Algorithm"],"prefix":"10.3390","volume":"25","author":[{"given":"Zhai","family":"Shi","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangwei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changjie","family":"Han","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongdong","family":"Song","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,19]]},"reference":[{"key":"ref_1","first-page":"20(008)","article-title":"The rise of domestic cotton pickers Warde opens a new chapter of cotton harvesting in Xinjiang","volume":"5","author":"Li","year":"2024","journal-title":"China Agric. Mech. Her."},{"key":"ref_2","first-page":"37","article-title":"Research status of cotton picker spindle wear analysis and surface strengthening technology","volume":"4","author":"He","year":"2024","journal-title":"Xinjiang Agric. Mech."},{"key":"ref_3","unstructured":"Yu, B. (2016). Design and Experimental Study of a Fire Warning Detection Device for Cotton Pickers. [Master\u2019s Thesis, Shihezi University]."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1039\/D2AN01523D","article-title":"Development of a mid-infrared sensor system for early fire identification in cotton harvesting operations","volume":"148","author":"Li","year":"2022","journal-title":"Analyst"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, W., Zhao, B., Gao, S., Zheng, Y., Zhou, L., and Liu, S. (2023). Development of Cotton Picker Fire Monitoring System Based on GA-BP Algorithm. Sensors, 23.","DOI":"10.3390\/s23125553"},{"key":"ref_6","first-page":"1","article-title":"A Review of Research on the Mechanisms of Cotton Picker Fires","volume":"54","author":"Shi","year":"2023","journal-title":"South. Agric. Mach."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_8","unstructured":"Ren, H. (2024). Research on the design method of PID furnace temperature control system based on BP neural network. Inf. Syst. Eng., 32\u201335."},{"key":"ref_9","first-page":"1","article-title":"Multi-sensor Fire Detection Algorithm Based on PSO-Nadam-BP Neural Network","volume":"35","author":"Sun","year":"2022","journal-title":"Ind. Control Comput."},{"key":"ref_10","first-page":"1345","article-title":"Fire early warning algorithm based on QPSO-BP neural network","volume":"39","author":"Gao","year":"2020","journal-title":"Fire Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"You, X., Zheng, Z., Yang, K., Yu, L., Liu, J., Chen, J., Lu, X., and Guo, S. (2024). A PSO-CNN-Based Deep Learning Model for Predicting Forest Fire Risk on a National Scale. Forests, 15.","DOI":"10.3390\/f15010086"},{"key":"ref_12","first-page":"289","article-title":"UAV Cluster Forest Fire Detection Method Based on PSO-GA Algorithm","volume":"59","author":"Huang","year":"2023","journal-title":"Comput. Eng. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wang, X., Chen, X., Zheng, J., Xiong, X., and Hu, X. (2024). A Novel HPSO-IGWO Algorithm for Rapidly Searching Optimal Fire Rescue Paths Based on IoT Architecture. IoT as a Service, Proceedings of the IoTaaS 2023, Nanjing, China, 27\u201329 October 2023, Springer. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.","DOI":"10.1007\/978-3-031-70507-6_21"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, L., Shi, C., and Zhang, F. (2024). Predicting Forest Fire Area Growth Rate Using an Ensemble Algorithm. Forests, 15.","DOI":"10.3390\/f15091493"},{"key":"ref_15","first-page":"22","article-title":"Three-dimensional digital modeling and finite element thermal analysis of cotton picking machine spindles","volume":"39","author":"Qiao","year":"2018","journal-title":"China Agrochem. J."},{"key":"ref_16","first-page":"176","article-title":"Modeling and computational analysis of heat production and heat transfer in key components of cotton pickers","volume":"46","author":"Li","year":"2018","journal-title":"Jiangsu Agric. Sci."},{"key":"ref_17","first-page":"2401","article-title":"Temperature control of proton exchange membrane fuel cell thermal management system based on APSO-BP-PID control strategy","volume":"54","author":"Shang","year":"2024","journal-title":"J. Jilin Univ. (Eng. Technol. Ed.)"},{"key":"ref_18","first-page":"94","article-title":"Traffic volume forecast model based on BP neural network optimized by improved sparrow search algorithm","volume":"56","author":"Chen","year":"2024","journal-title":"J. Harbin Inst. Technol."},{"key":"ref_19","unstructured":"Han, Z., Liu, Z., Chang, T., Pei, C., Zhang, P., and Zhang, J. (2024). Optimization of Wear Reduction of Salix Cheilophila Cutting Tools\u2014Based on PSO-BP Neural Network Combined with GA Algorithm. J. Agric. Mech. Res., 1\u20137."},{"key":"ref_20","first-page":"898","article-title":"An Improved Genetic Algorithm Variation Operator","volume":"50","author":"Wang","year":"2019","journal-title":"J. Shandong Agric. Univ. (Nat. Sci. Ed.)"},{"key":"ref_21","unstructured":"Wang, H. (2024). Research on Optimized Design of Fuel Cell Structure Based on Genetic Algorithm. Energy Environ., 6\u201345."},{"key":"ref_22","unstructured":"Guo, Z. (2022). Research on Feature Selection Method Based on Improved Whale Optimization Algorithm. [Master\u2019s Thesis, Liaoning University of Engineering and Technology]."},{"key":"ref_23","first-page":"117","article-title":"Performance prediction of natural gas hydrogen-doped engines based on IMPSO-BPNN","volume":"24","author":"Duan","year":"2024","journal-title":"J. Transp. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/2\/564\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:31:43Z","timestamp":1759919503000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/2\/564"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,19]]},"references-count":23,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["s25020564"],"URL":"https:\/\/doi.org\/10.3390\/s25020564","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,1,19]]}}}