{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T01:50:10Z","timestamp":1776909010210,"version":"3.51.2"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T00:00:00Z","timestamp":1682726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Project of Guangdong Eco-Engineering Polytechnic","award":["2020kykt-xj-zk10"],"award-info":[{"award-number":["2020kykt-xj-zk10"]}]},{"name":"Scientific Research Project of Guangdong Eco-Engineering Polytechnic","award":["pdjh2021b0850"],"award-info":[{"award-number":["pdjh2021b0850"]}]},{"name":"Scientific Research Project of Guangdong Eco-Engineering Polytechnic","award":["2018KJCX003"],"award-info":[{"award-number":["2018KJCX003"]}]},{"name":"Scientific Research Project of Guangdong Eco-Engineering Polytechnic","award":["2022A1515140162"],"award-info":[{"award-number":["2022A1515140162"]}]},{"name":"Scientific Research Project of Guangdong Eco-Engineering Polytechnic","award":["2022A1515140013"],"award-info":[{"award-number":["2022A1515140013"]}]},{"name":"Scientific Research Project of Guangdong Eco-Engineering Polytechnic","award":["2018KJCX003"],"award-info":[{"award-number":["2018KJCX003"]}]},{"name":"Scientific Research Project of Guangdong Eco-Engineering Polytechnic","award":["2020KJCX003"],"award-info":[{"award-number":["2020KJCX003"]}]},{"name":"Special fund project of Guangdong science and technology innovation strategy","award":["2020kykt-xj-zk10"],"award-info":[{"award-number":["2020kykt-xj-zk10"]}]},{"name":"Special fund project of Guangdong science and technology innovation strategy","award":["pdjh2021b0850"],"award-info":[{"award-number":["pdjh2021b0850"]}]},{"name":"Special fund project of Guangdong science and technology innovation strategy","award":["2018KJCX003"],"award-info":[{"award-number":["2018KJCX003"]}]},{"name":"Special fund project of Guangdong science and technology innovation strategy","award":["2022A1515140162"],"award-info":[{"award-number":["2022A1515140162"]}]},{"name":"Special fund project of Guangdong science and technology innovation strategy","award":["2022A1515140013"],"award-info":[{"award-number":["2022A1515140013"]}]},{"name":"Special fund project of Guangdong science and technology innovation strategy","award":["2018KJCX003"],"award-info":[{"award-number":["2018KJCX003"]}]},{"name":"Special fund project of Guangdong science and technology innovation strategy","award":["2020KJCX003"],"award-info":[{"award-number":["2020KJCX003"]}]},{"name":"Characteristic Innovation Projects of Department of Education of Guangdong Province","award":["2020kykt-xj-zk10"],"award-info":[{"award-number":["2020kykt-xj-zk10"]}]},{"name":"Characteristic Innovation Projects of Department of Education of Guangdong Province","award":["pdjh2021b0850"],"award-info":[{"award-number":["pdjh2021b0850"]}]},{"name":"Characteristic Innovation Projects of Department of Education of Guangdong Province","award":["2018KJCX003"],"award-info":[{"award-number":["2018KJCX003"]}]},{"name":"Characteristic Innovation Projects of Department of Education of Guangdong Province","award":["2022A1515140162"],"award-info":[{"award-number":["2022A1515140162"]}]},{"name":"Characteristic Innovation Projects of Department of Education of Guangdong Province","award":["2022A1515140013"],"award-info":[{"award-number":["2022A1515140013"]}]},{"name":"Characteristic Innovation Projects of Department of Education of Guangdong Province","award":["2018KJCX003"],"award-info":[{"award-number":["2018KJCX003"]}]},{"name":"Characteristic Innovation Projects of Department of Education of Guangdong Province","award":["2020KJCX003"],"award-info":[{"award-number":["2020KJCX003"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2020kykt-xj-zk10"],"award-info":[{"award-number":["2020kykt-xj-zk10"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["pdjh2021b0850"],"award-info":[{"award-number":["pdjh2021b0850"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2018KJCX003"],"award-info":[{"award-number":["2018KJCX003"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2022A1515140162"],"award-info":[{"award-number":["2022A1515140162"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2022A1515140013"],"award-info":[{"award-number":["2022A1515140013"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2018KJCX003"],"award-info":[{"award-number":["2018KJCX003"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2020KJCX003"],"award-info":[{"award-number":["2020KJCX003"]}]},{"name":"Guangdong Forestry Science and Technology Innovation Project","award":["2020kykt-xj-zk10"],"award-info":[{"award-number":["2020kykt-xj-zk10"]}]},{"name":"Guangdong Forestry Science and Technology Innovation Project","award":["pdjh2021b0850"],"award-info":[{"award-number":["pdjh2021b0850"]}]},{"name":"Guangdong Forestry Science and Technology Innovation Project","award":["2018KJCX003"],"award-info":[{"award-number":["2018KJCX003"]}]},{"name":"Guangdong Forestry Science and Technology Innovation Project","award":["2022A1515140162"],"award-info":[{"award-number":["2022A1515140162"]}]},{"name":"Guangdong Forestry Science and Technology Innovation Project","award":["2022A1515140013"],"award-info":[{"award-number":["2022A1515140013"]}]},{"name":"Guangdong Forestry Science and Technology Innovation Project","award":["2018KJCX003"],"award-info":[{"award-number":["2018KJCX003"]}]},{"name":"Guangdong Forestry Science and Technology Innovation Project","award":["2020KJCX003"],"award-info":[{"award-number":["2020KJCX003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring and early warning technology for forest fires is crucial. An early warning\/monitoring system for forest fires was constructed based on deep learning and the internet of things. Forest fire recognition was improved by combining the size, color, and shape characteristics of the flame, smoke, and area. Complex upper-layer fire-image features were extracted, improving the input conversion by building a forest fire risk prediction model based on an improved dynamic convolutional neural network. The proposed back propagation neural network fire (BPNNFire) algorithm calculated the image processing speed and delay rate, and data were preprocessed to remove noise. The model recognized forest fire images, and the classifier classified them to distinguish images with and without fire. Fire images were classified locally for feature extraction. Forest fire images were stored on a remote server. Existing algorithms were compared, and BPNNFire provided real-time accurate forest fire recognition at a low frame rate with 84.37% accuracy, indicating superior recognition. The maximum relative error between the measured and actual values for real-time online monitoring of forest environment indicators, such as air temperature and humidity, was 5.75%. The packet loss rate of the forest fire monitoring network was 5.99% at Longshan Forest Farm and 2.22% at Longyandong Forest Farm.<\/jats:p>","DOI":"10.3390\/rs15092365","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:10:03Z","timestamp":1682943003000},"page":"2365","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT"],"prefix":"10.3390","volume":"15","author":[{"given":"Shaoxiong","family":"Zheng","sequence":"first","affiliation":[{"name":"Guangdong Eco-Engineering Polytechnic, Guangzhou 510520, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Guangdong Academy of Forestry Sciences, Guangzhou 510520, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zepeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Guangdong Academy of Forestry Sciences, Guangzhou 510520, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangxiang","family":"Wan","sequence":"additional","affiliation":[{"name":"Guangdong Eco-Engineering Polytechnic, Guangzhou 510520, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Hu","sequence":"additional","affiliation":[{"name":"Guangdong Eco-Engineering Polytechnic, Guangzhou 510520, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixing","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhujiang College, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangjun","family":"Zou","sequence":"additional","affiliation":[{"name":"Foshan-Zhongke Innovation Research Institute of Intelligent Agriculture and Robotics, Foshan 52800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shihong","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong Eco-Engineering Polytechnic, Guangzhou 510520, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s10342-010-0392-1","article-title":"Assessing natural hazards in forestry for risk management: A review","volume":"130","author":"Hanewinkel","year":"2011","journal-title":"Eur. J. For. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.protcy.2012.03.008","article-title":"Wireless sensor networks and fusion information methods for forest fire detection","volume":"3","author":"Tafoya","year":"2012","journal-title":"Procedia Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1016\/j.procs.2013.06.104","article-title":"Using wireless sensor networks for reliable forest fires detection","volume":"19","author":"Bouabdellah","year":"2013","journal-title":"Procedia Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5177","DOI":"10.1007\/s11276-020-02393-1","article-title":"EEFFL: Energy efficient data forwarding for forest fire detection using localization technique in wireless sensor network","volume":"26","author":"Vikram","year":"2020","journal-title":"Wirel. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nebot, \u00c0., and Mugica, F. (2021). Forest Fire Forecasting Using Fuzzy Logic Models. Forests, 12.","DOI":"10.3390\/f12081005"},{"key":"ref_6","first-page":"1","article-title":"Application of GIS and AHP Method in Forest Fire Risk Zone Mapping: A Study of the Parambikulam Tiger Reserve, Kerala, India","volume":"5","author":"Nikhil","year":"2021","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s10694-020-01056-z","article-title":"A survey of machine learning algorithms based forest fires prediction and detection systems","volume":"57","author":"Faroudja","year":"2021","journal-title":"Fire Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., and Grammalidis, N. (2020). A review on early forest fire detection systems using optical remote sensing. Sensors, 20.","DOI":"10.3390\/s20226442"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1007\/s10694-020-00986-y","article-title":"Video flame and smoke based fire detection algorithms: A literature review","volume":"56","author":"Gaur","year":"2020","journal-title":"Fire Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Moumgiakmas, S.S., Samatas, G.G., and Papakostas, G.A. (2021). Computer Vision for Fire Detection on UAVs\u2014From Software to Hardware. Future Internet, 13.","DOI":"10.3390\/fi13080200"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1007\/s11276-018-1872-3","article-title":"A novel approach of WSN routing protocols comparison for forest fire detection","volume":"26","author":"Moussa","year":"2020","journal-title":"Wireless Netw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9083","DOI":"10.1007\/s11042-019-07785-w","article-title":"Convolutional neural network based early fire detection","volume":"79","author":"Saeed","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2561","DOI":"10.1007\/s11277-019-06697-0","article-title":"Semisupervised classification based clustering approach in WSN for forest fire detection","volume":"109","author":"Sinha","year":"2019","journal-title":"Wirel. Pers. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.procs.2020.07.061","article-title":"Wireless sensor network for forest fire detection","volume":"175","author":"Varela","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2018.04.053","article-title":"A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing","volume":"212","author":"Yebra","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"116114","DOI":"10.1016\/j.eswa.2021.116114","article-title":"Attention based CNN model for fire detection and localization in real-world images","volume":"189","author":"Majid","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"84","DOI":"10.22399\/ijcesen.950045","article-title":"Comprehensive analysis of forest fire detection using deep learning models and conventional machine learning algorithms","volume":"7","author":"Kukuk","year":"2021","journal-title":"Int. J. Comput. Exp. Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Avazov, K., Hyun, A.E., Sami, S.A.A., Khaitov, A., Abdusalomov, A.B., and Cho, Y.I. (2023). Forest Fire Detection and Notification Method Based on AI and IoT Approaches. Future Internet, 15.","DOI":"10.3390\/fi15020061"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"115158","DOI":"10.1016\/j.engstruct.2022.115158","article-title":"Novel visual crack width measurement based on backbone double-scale features for improved detection automation","volume":"274","author":"Tang","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_20","first-page":"1587","article-title":"Real-time forest fire monitoring system using unmanned aerial vehicle","volume":"13","author":"Wardihani","year":"2018","journal-title":"J. Eng. Sci. Technol."},{"key":"ref_21","first-page":"1","article-title":"Optimization strategies of fruit detection to overcome the challenge of unstructured background in field orchard environment: A review","volume":"274","author":"Tang","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"117723","DOI":"10.1016\/j.foreco.2019.117723","article-title":"A Bayesian network model for prediction and analysis of possible forest fire causes","volume":"457","author":"Sevinc","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"236","DOI":"10.18196\/jrc.v3i3.14128","article-title":"Design and Implementation of LoRa-Based Forest Fire Monitoring System","volume":"3","author":"Apriani","year":"2022","journal-title":"J. Robot. Control"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Park, M., Tran, D.Q., Lee, S., and Park, S. (2021). Multilabel Image Classification with Deep Transfer Learning for Decision Support on Wildfire Response. Remote Sens., 13.","DOI":"10.3390\/rs13193985"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1038\/s41598-021-03882-9","article-title":"Forest fire detection system using wireless sensor networks and machine learning","volume":"12","author":"Dampage","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_26","first-page":"1","article-title":"Machine learning regression techniques to predict burned area of forest fires","volume":"16","author":"Elshewey","year":"2021","journal-title":"Int. J. Soft Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1163839","DOI":"10.3389\/fpls.2023.1163839","article-title":"Precision control technology and application in agricultural pest and disease control","volume":"14","author":"Tang","year":"2023","journal-title":"Front. Plant Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Guede-Fern\u00e1ndez, F., Martins, L., de Almeida, R.V., Gamboa, H., and Vieira, P. (2021). A deep learning based object identification system for forest fire detection. Fire, 4.","DOI":"10.3390\/fire4040075"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, C., Tang, Y., Zou, X., Zhang, P., Lin, J., Lian, G., and Pan, Y. (2022). A Novel Agricultural Machinery Intelligent Design System Based on Integrating Image Processing and Knowledge Reasoning. Appl. Sci., 12.","DOI":"10.3390\/app12157900"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Naderpour, M., Rizeei, H.M., and Ramezani, F. (2021). Forest fire risk prediction: A spatial deep neural network-based framework. Remote Sens., 13.","DOI":"10.3390\/rs13132513"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"118573","DOI":"10.1016\/j.eswa.2022.118573","article-title":"Fruit detection and positioning technology for a Camellia oleifera C. Abel orchard based on improved YOLOv4-tiny model and binocular stereo vision","volume":"211","author":"Tang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bajracharya, B., Thapa, R.B., and Matin, M.A. (2021). Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region, Springer.","DOI":"10.1007\/978-3-030-73569-2"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Miller, E.A. (2020). A Conceptual Interpretation of the Drought Code of the Canadian Forest Fire Weather Index System. Fire, 3.","DOI":"10.3390\/fire3020023"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Tang, Y., Zou, X., Wu, M., Tang, W., Meng, F., Zhang, Y., and Kang, H. (2022). Adaptive Active Positioning of Camellia oleifera Fruit Picking Points: Classical Image Processing and YOLOv7 Fusion Algorithm. Appl. Sci., 12.","DOI":"10.3390\/app122412959"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"100573","DOI":"10.1016\/j.polar.2020.100573","article-title":"Impacts and management of forest fires in the Republic of Sakha, Russia: A local perspective for a global problem","volume":"27","author":"Narita","year":"2021","journal-title":"Polar Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.jenvman.2019.01.108","article-title":"Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam)","volume":"237","author":"Bui","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1007\/s11676-021-01379-9","article-title":"Distribution prediction of moisture content of dead fuel on the forest floor of Maoershan national forest, China using a LoRa wireless network","volume":"33","author":"Peng","year":"2022","journal-title":"J. For. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"142844","DOI":"10.1016\/j.scitotenv.2020.142844","article-title":"Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series","volume":"764","author":"Michael","year":"2021","journal-title":"Sci. Total Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2365\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:26:52Z","timestamp":1760124412000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2365"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,29]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092365"],"URL":"https:\/\/doi.org\/10.3390\/rs15092365","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,29]]}}}