# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 4 Conclusions  
This study conducted comprehensive tests and analyses in Longshan and Longyan-dong Forest Farms to verify system stability and functionality. The research results are as follows:  
* The system can typically transmit the forest environment data monitored by the ground WSN, and the UAV can generally return fire images above the forest and provide a prompt early warning, meeting the needs of forest fire monitoring;
* Multiple algorithms compared the processing speed of a video with a processing time of 4 min and 16 s. The video had 29 images/s, and the size of each frame was 960\(\times\)540, for a total of 7424 images. The processing speed and delay rate of the video images were calculated using the BPNNFire algorithm and other algorithms. The test results revealed that the BPNNFire algorithm's judgment accuracy rate was 84.37%, indicating that this algorithm was superior to other recognized algorithms;
* The real-time online monitoring of forest environmental indicators for three months indicates that the packet loss rate of the forest fire monitoring network was 5.99%for Longshan Forest Farm and 2.22% for Longyandong Forest Farm. The constructed hardware equipment and embedded routing protocol could be applied to an unattended situation in the forest fire monitoring field to ensure long-term stable system operation. The maximum relative error between the measured temperature and humidity values and the actual value was 5.75%, indicating that the forest fire monitoring/early warning system could stably receive and transmit forest environmental data, and that the system connectivity was good.  
Although the improved BPNNFire algorithm, as applied to forest fire detection, achieved good results, the detection accuracy and performance of the forest fire recognition model must still be improved for further optimization. Future research could explore how to reduce the model complexity, simplify the model structure, and optimize the model parameters, ensuring a lightweight model the calculation efficiency.  
Conceptualization, S.Z., X.Z. and S.C.; methodology, S.Z., X.Z., S.C. and Y.Z.; software, S.Z. and P.G.; validation, S.Z., X.Z., P.G. and Y.Z.; formal analysis, S.Z., P.G. and Z.W.; investigation, S.Z., X.Z., S.C., L.W., F.H. and P.G.; resources, S.Z., S.C. and Y.Z.; data curation, S.Z., P.G. and Y.Z.; writing--original draft preparation, S.Z., P.G. and X.Z.; writing--review and editing, S.Z., S.C., P.G. and X.Z.; visualization, S.Z., P.G., F.H. and X.Z.; supervision, S.C., W.W. and Y.Z.; project administration, S.Z., S.C., X.Z., Y.Z. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Research Project of Guangdong Eco-Engineering Polytechnic, grant number 2020 kykt-xj-zk10; Special fund project of Guangdong science and technology innovation strategy, grant number pdjh2021b0850; Characteristic Innovation Projects of Department of Education of Guangdong Province, grant number 2018 KJCX003; Guangdong Basic and Applied Basic Research Foundation, grant numbers 2022A1515140162 and 2022A1515140013; Guangdong Forestry Science and Technology Innovation Project, grant number 2018 KJCX003 and 2020 KJCX003.  
The data can be requested from the corresponding authors.  
We would like to thank the anonymous reviewers for their critical comments and suggestions for improving the manuscript.  
The authors declare that there are no conflict of interest.