{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:36:39Z","timestamp":1775230599572,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T00:00:00Z","timestamp":1673308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Forest fires are the main cause of desertification, and they have a disastrous impact on agricultural and forest ecosystems. Modern fire detection and warning systems rely on several techniques: satellite monitoring, sensor networks, image processing, data fusion, etc. Recently, Artificial Intelligence (AI) algorithms have been applied to fire recognition systems, enhancing their efficiency and reliability. However, these devices usually need constant data transmission along with a proper amount of computing power, entailing high costs and energy consumption. This paper presents the prototype of a Video Surveillance Unit (VSU) for recognising and signalling the presence of forest fires by exploiting two embedded Machine Learning (ML) algorithms running on a low power device. The ML models take audio samples and images as their respective inputs, allowing for timely fire detection. The main result is that while the performances of the two models are comparable when they work independently, their joint usage according to the proposed methodology provides a higher accuracy, precision, recall and F1 score (96.15%, 92.30%, 100.00%, and 96.00%, respectively). Eventually, each event is remotely signalled by making use of the Long Range Wide Area Network (LoRaWAN) protocol to ensure that the personnel in charge are able to operate promptly.<\/jats:p>","DOI":"10.3390\/s23020783","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T04:59:58Z","timestamp":1673413198000},"page":"783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Fight Fire with Fire: Detecting Forest Fires with Embedded Machine Learning Models Dealing with Audio and Images on Low Power IoT Devices"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6311-7332","authenticated-orcid":false,"given":"Giacomo","family":"Peruzzi","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Padova, 35131 Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3991-8858","authenticated-orcid":false,"given":"Alessandro","family":"Pozzebon","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Padova, 35131 Padova, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2437-8780","authenticated-orcid":false,"given":"Mattia","family":"Van Der Meer","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"ref_1","unstructured":"San-Miguel-Ayanz, J., Durrant, T., Boca, R., Maianti, P., Liberta, G., Artes Vivancos, T., Jacome Felix Oom, D., Branco, A., de Rigo, D., and Ferrari, D. (2021). Advance Report on Wildfires in Europe, Middle East and North Africa, Publications Office of the European Union."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pham, H.T., Nguyen, M.A., and Sun, C.C. (2019, January 3\u20136). AIoT solution survey and comparison in machine learning on low-cost microcontroller. Proceedings of the 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Taipei, Taiwan.","DOI":"10.1109\/ISPACS48206.2019.8986357"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Merenda, M., Porcaro, C., and Iero, D. (2020). Edge machine learning for ai-enabled iot devices: A review. Sensors, 20.","DOI":"10.3390\/s20092533"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TII.2021.3076077","article-title":"A novel two-stage unsupervised fault recognition framework combining feature extraction and fuzzy clustering for collaborative AIoT","volume":"18","author":"Hu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14366","DOI":"10.1109\/JIOT.2021.3078166","article-title":"Real-time water quality monitoring and estimation in AIoT for freshwater biodiversity conservation","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zualkernan, I., Judas, J., Mahbub, T., Bhagwagar, A., and Chand, P. (2021, January 27\u201328). An aiot system for bat species classification. Proceedings of the 2020 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), Bali, Indonesia.","DOI":"10.1109\/IoTaIS50849.2021.9359704"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bertocco, M., Fort, A., Landi, E., Mugnaini, M., Parri, L., Peruzzi, G., and Pozzebon, A. (2022, January 4\u20136). Roller Bearing Failures Classification with Low Computational Cost Embedded Machine Learning. Proceedings of the 2022 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), Modena, Italy.","DOI":"10.1109\/MetroAutomotive54295.2022.9855137"},{"key":"ref_8","first-page":"212","article-title":"Role of machine learning algorithms in forest fire management: A literature review","volume":"5","author":"Arif","year":"2021","journal-title":"J. Robot. Autom."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"124","DOI":"10.14419\/ijet.v7i2.7.10277","article-title":"IoT based forest fire detection system","volume":"7","author":"Basu","year":"2018","journal-title":"Int. J. Eng. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Deve, K., Hancke, G.P., and Silva, B.J. (2016, January 23\u201326). Design of a smart fire detection system. Proceedings of the IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy.","DOI":"10.1109\/IECON.2016.7794000"},{"key":"ref_11","first-page":"2994","article-title":"A smart fire detection system using IoT technology with automatic water sprinkler","volume":"11","author":"Alqourabah","year":"2021","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kadri, B., Bouyeddou, B., and Moussaoui, D. (2018, January 24\u201325). Early fire detection system using wireless sensor networks. Proceedings of the 2018 International Conference on Applied Smart Systems (ICASS), Medea, Algeria.","DOI":"10.1109\/ICASS.2018.8651977"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Baldo, D., Mecocci, A., Parrino, S., Peruzzi, G., and Pozzebon, A. (2021). A multi-layer lorawan infrastructure for smart waste management. Sensors, 21.","DOI":"10.3390\/s21082600"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102332","DOI":"10.1016\/j.scs.2020.102332","article-title":"An integrated fire detection system using IoT and image processing technique for smart cities","volume":"61","author":"Sharma","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kinaneva, D., Hristov, G., Raychev, J., and Zahariev, P. (2019, January 20\u201324). Early forest fire detection using drones and artificial intelligence. Proceedings of the 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2019.8756696"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jiao, Z., Zhang, Y., Xin, J., Mu, L., Yi, Y., Liu, H., and Liu, D. (2019, January 23\u201327). A deep learning based forest fire detection approach using UAV and YOLOv3. Proceedings of the 2019 1st International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China.","DOI":"10.1109\/ICIAI.2019.8850815"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"165491","DOI":"10.1016\/j.ijleo.2020.165491","article-title":"Research on the identification method for the forest fire based on deep learning","volume":"223","author":"Liu","year":"2020","journal-title":"Optik"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, R., Lin, H., Lu, K., Cao, L., and Liu, Y. (2021). A forest fire detection system based on ensemble learning. Forests, 12.","DOI":"10.3390\/f12020217"},{"key":"ref_19","first-page":"496","article-title":"Early forest fire detection system using wireless sensor network and deep learning","volume":"11","author":"Benzekri","year":"2020","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3390\/fire5010023","article-title":"Real-time forest fire detection framework based on artificial intelligence using color probability model and motion feature analysis","volume":"5","author":"Harjoko","year":"2022","journal-title":"Fire"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, S., Gao, D., Lin, H., and Sun, Q. (2019). Wildfire detection using sound spectrum analysis based on the internet of things. Sensors, 19.","DOI":"10.3390\/s19235093"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, Y., Mu, L., Xin, J., Yu, Z., Liu, H., and Xie, G. (2021, January 8\u201311). Early Forest Fire Detection Based on Deep Learning. Proceedings of the 2021 3rd International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China.","DOI":"10.1109\/IAI53119.2021.9619342"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Alves, J., Soares, C., Torres, J.M., Sobral, P., and Moreira, R.S. (2019, January 24\u201326). Automatic forest fire detection based on a machine learning and image analysis pipeline. Proceedings of the World Conference on Information Systems and Technologies, Cairo, Egypt.","DOI":"10.1007\/978-3-030-16184-2_24"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chopde, A., Magon, A., and Bhatkar, S. (2022, January 10\u201312). Forest Fire Detection and Prediction from Image Processing Using RCNN. Proceedings of the 7th World Congress on Civil, Structural, and Environmental Engineering, Virtual.","DOI":"10.11159\/iceptp22.204"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"012014","DOI":"10.1088\/1742-6596\/1768\/1\/012014","article-title":"Image processing based forest fire detection using infrared camera","volume":"1768","author":"Najib","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, H.T., Downey, A.R., and Bakos, J.D. (2022). Audio-Based Wildfire Detection on Embedded Systems. Electronics, 11.","DOI":"10.3390\/electronics11091417"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fort, A., Peruzzi, G., and Pozzebon, A. (2021, January 7\u20139). Quasi-Real Time Remote Video Surveillance Unit for LoRaWAN-based Image Transmission. Proceedings of the 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0&IoT), Rome, Italy.","DOI":"10.1109\/MetroInd4.0IoT51437.2021.9488519"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Oroceo, P.P., Kim, J.I., Caliwag, E.M.F., Kim, S.H., and Lim, W. (2022). Optimizing Face Recognition Inference with a Collaborative Edge\u2013Cloud Network. Sensors, 22.","DOI":"10.3390\/s22218371"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dhou, S., Alnabulsi, A., Al-Ali, A., Arshi, M., Darwish, F., Almaazmi, S., and Alameeri, R. (2022). An IoT machine learning-based mobile sensors unit for visually impaired people. Sensors, 22.","DOI":"10.3390\/s22145202"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Munteanu, D., Moina, D., Zamfir, C.G., Petrea, S.M., Cristea, D.S., and Munteanu, N. (2022). Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models. Sensors, 22.","DOI":"10.3390\/s22239536"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Peruzzi, G., Galli, A., and Pozzebon, A. (2022, January 18\u201320). A Novel Methodology to Remotely and Early Diagnose Sleep Bruxism by Leveraging on Audio Signals and Embedded Machine Learning. Proceedings of the 2022 IEEE International Symposium on Measurements & Networking (M&N), Padua, Italy.","DOI":"10.1109\/MN55117.2022.9887782"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hemdan, E.E.D., El-Shafai, W., and Sayed, A. (2022). CR19: A framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications. J. Ambient. Intell. Humaniz. Comput., 1\u201313.","DOI":"10.1007\/s12652-022-03732-0"},{"key":"ref_33","unstructured":"Pahar, M., and Niesler, T. (2023, January 03). Machine Learning Based COVID-19 Detection from Smartphone Recordings: Cough, Breath and Speech. Available online: https:\/\/www.researchgate.net\/publication\/350673813_Machine_Learning_based_COVID-19_Detection_from_Smartphone_Recordings_Cough_Breath_and_Speech."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"da Silva, B., Happi, A.W., Braeken, A., and Touhafi, A. (2019). Evaluation of classical machine learning techniques towards urban sound recognition on embedded systems. Appl. Sci., 9.","DOI":"10.3390\/app9183885"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ravi, P., Syam, U., and Kapre, N. (2016, January 17\u201322). Preventive detection of mosquito populations using embedded machine learning on low power iot platforms. Proceedings of the 7th Annual Symposium on Computing for Development, Nairobi, Kenya.","DOI":"10.1145\/3001913.3001917"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"67594","DOI":"10.1109\/ACCESS.2018.2877523","article-title":"Smart audio sensors in the internet of things edge for anomaly detection","volume":"6","author":"Antonini","year":"2018","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1109\/TASLP.2021.3133208","article-title":"FSD50K: An open dataset of human-labeled sound events","volume":"30","author":"Fonseca","year":"2022","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Piczak, K.J. (2015, January 26\u201330). ESC: Dataset for Environmental Sound Classification. Proceedings of the 23rd Annual ACM Conference on Multimedia, Brisbane, Australia.","DOI":"10.1145\/2733373.2806390"},{"key":"ref_39","unstructured":"Peruzzi, G., Pozzebon, A., and Van Der Meer, M. (2022, November 18). Audio Dataset. Available online: https:\/\/drive.google.com\/file\/d\/15PQ-my8cA1blUIbAGRY8Jhq_d8Z7qim7\/view."},{"key":"ref_40","unstructured":"Dincer, B. (2022, November 23). Wildfire Detection Image Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/brsdincer\/wildfire-detection-image-data."},{"key":"ref_41","unstructured":"Peruzzi, G., Pozzebon, A., and Van Der Meer, M. (2022, November 18). Picture Dataset. Available online: https:\/\/drive.google.com\/file\/d\/1QEAt4JiNxu5zZpXkWVnJm5sgtZm15Cf4\/view?usp=share_link."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Parri, L., Parrino, S., Peruzzi, G., and Pozzebon, A. (2020, January 25\u201328). A LoRaWAN network infrastructure for the remote monitoring of offshore sea farms. Proceedings of the 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Dubrovnik, Croatia.","DOI":"10.1109\/I2MTC43012.2020.9128370"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Miranda, I.D., Diacon, A.H., and Niesler, T.R. (2019, January 23\u201327). A comparative study of features for acoustic cough detection using deep architectures. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856412"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Koike, T., Qian, K., Kong, Q., Plumbley, M.D., Schuller, B.W., and Yamamoto, Y. (2020, January 20\u201324). Audio for audio is better? An investigation on transfer learning models for heart sound classification. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175450"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kutsumi, Y., Kanegawa, N., Zeida, M., Matsubara, H., and Murayama, N. (2023). Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone. Sensors, 23.","DOI":"10.3390\/s23010407"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1007\/s42045-020-00042-x","article-title":"Cooperative abnormal sound event detection in end-edge-cloud orchestrated systems","volume":"3","author":"Wang","year":"2020","journal-title":"CCF Trans. Netw."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.neucom.2021.08.115","article-title":"HuRAI: A brain-inspired computational model for human-robot auditory interface","volume":"465","author":"Wu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_48","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3162270","article-title":"Smart Gravimetric System for Enhanced Security of Accesses to Public Places Embedding a MobileNet Neural Network Classifier","volume":"71","author":"Addabbo","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"26","DOI":"10.18372\/1990-5548.72.16939","article-title":"Camera Image Processing on ESP32 Microcontroller with Help of Convolutional Neural Network","volume":"2","author":"Sineglazov","year":"2022","journal-title":"Electron. Control Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bilang, J.M.D., Balbuena, P.A.A.L., and Villaverde, J.F. (2021, January 28\u201330). Cactaceae Detection Using MobileNet Architecture. Proceedings of the 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines.","DOI":"10.1109\/HNICEM54116.2021.9731868"},{"key":"ref_52","first-page":"79","article-title":"Analysis Quality of Corn Based on IoT, SSD Mobilenet Models and Histogram","volume":"11","author":"Audy","year":"2022","journal-title":"J. Nas. Tek. Elektro Dan Teknol. Inf. Vol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zheng, W.C., Lee, J.S., and Sun, Y.H. (2021, January 15\u201317). Development of AI-based Recycling Bins Using MobileNet-SSD Networks. Proceedings of the 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Penghu, Taiwan.","DOI":"10.1109\/ICCE-TW52618.2021.9602955"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Rabano, S.L., Cabatuan, M.K., Sybingco, E., Dadios, E.P., and Calilung, E.J. (December, January 29). Common garbage classification using mobilenet. Proceedings of the 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines.","DOI":"10.1109\/HNICEM.2018.8666300"},{"key":"ref_55","unstructured":"Kokilavani, V. (2022, January 15\u201316). Intelligent Face Mask and Body Temperature Detection System using Machine Learning Algorithm. Proceedings of the 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hossain, D., Imtiaz, M.H., Ghosh, T., Bhaskar, V., and Sazonov, E. (2020, January 20\u201324). Real-time food intake monitoring using wearable egocnetric camera. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175497"},{"key":"ref_57","unstructured":"Peruzzi, G., Pozzebon, A., and Van Der Meer, M. (2022, December 01). Test Video. Available online: https:\/\/drive.google.com\/file\/d\/1Hi2gs4mkrFibULaHfVDzgJZgVaVUYf6L\/view?usp=share_link."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/783\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:05:27Z","timestamp":1760119527000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,10]]},"references-count":57,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020783"],"URL":"https:\/\/doi.org\/10.3390\/s23020783","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,10]]}}}