{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:03:14Z","timestamp":1790254994820,"version":"4.1.0"},"reference-count":36,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic Research Project of Shanxi Province","award":["202303021222066"],"award-info":[{"award-number":["202303021222066"]}]},{"name":"Middle-aged and Young Elite Innovative Talent Cultivation Project","award":["SXAUKY2024002"],"award-info":[{"award-number":["SXAUKY2024002"]}]},{"name":"Science and Technology Innovation Promotion Project of Shanxi Agricultural University","award":["CXGC2026058"],"award-info":[{"award-number":["CXGC2026058"]}],"id":[{"id":"https:\/\/ror.org\/05e9f5362","id-type":"ROR","asserted-by":"crossref"}]},{"award":["CXGC2026058"],"award-info":[{"award-number":["CXGC2026058"]}],"id":[{"id":"https:\/\/ror.org\/05e9f5362","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Lvliang City Key Research and Development Program","award":["2025NY17"],"award-info":[{"award-number":["2025NY17"]}]},{"name":"Graduate Quality Engineering Project of Shanxi Agricultural University","award":["2025YZLGC033"],"award-info":[{"award-number":["2025YZLGC033"]}],"id":[{"id":"https:\/\/ror.org\/05e9f5362","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Graduate Quality Engineering Project of Shanxi Agricultural University","award":["2025JG056"],"award-info":[{"award-number":["2025JG056"]}],"id":[{"id":"https:\/\/ror.org\/05e9f5362","id-type":"ROR","asserted-by":"crossref"}]},{"award":["2025YZLGC033"],"award-info":[{"award-number":["2025YZLGC033"]}],"id":[{"id":"https:\/\/ror.org\/05e9f5362","id-type":"ROR","asserted-by":"publisher"}]},{"award":["2025JG056"],"award-info":[{"award-number":["2025JG056"]}],"id":[{"id":"https:\/\/ror.org\/05e9f5362","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>The early detection of wildland forest fires is a crucial task for protecting vulnerable plant ecosystems, yet it remains physically challenging due to the extreme limitations imposed by micro-scale targets and severe environmental interference within dense vegetation. Traditional monocular vision systems typically rely on statistical fitting and are highly prone to false alarms when facing complex optical interference from vegetation canopies, such as penetrating sunlight through foliage and specular reflections. Although multispectral fusion aims to mitigate this issue, existing dual-stream architectures frequently fall into the \u201cheterogeneous negative transfer\u201d trap, where modal conflicts and misaligned physical priors degrade the detection performance. To address these issues, this study introduces a causality-driven and physical-feature-decoupled YOLO (CPD-YOLOv8) framework. Adhering to the principle of physical fidelity, we implement an early R-G-NIR channel replacement strategy to ensure the fundamental physical alignment of spatial and thermodynamic properties. Furthermore, an Adaptive Cross-Modal Attention (ACMA) module is incorporated to facilitate non-destructive feature decoupling, and a Regional Causal-Preserving Downsampling (RCPD) module is designed to intercept environmental confounders (e.g., high-frequency leaf reflections) at the local micro-scale. This framework explores the transition of detection methods from pure statistical correlation toward physical causality. Experimental results on the Corsican wildland multispectral forest fire dataset demonstrate that CPD-YOLOv8 yields an mAP@0.5 of 70.3% and a precision of 87.1%. By mitigating the constraint between precision and recall, this method provides a low-false-alarm solution, offering a technical reference for proactive vegetation management and the reliable deployment of fire early-warning algorithms in real-world complex wildland terrains.<\/jats:p>","DOI":"10.3390\/plants15192927","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T12:07:23Z","timestamp":1790251643000},"page":"2927","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CPD-YOLOv8: A Causality-Driven Physical Modality Fusion Framework for Early Micro-Fire Detection to Protect Complex Wildland Vegetation"],"prefix":"10.3390","volume":"15","author":[{"given":"Juan","family":"Liu","sequence":"first","affiliation":[{"name":"Shanxi Agricultural University, Jinzhong 030800, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"given":"Ruitong","family":"Yao","sequence":"additional","affiliation":[{"name":"Shanxi Agricultural University, Jinzhong 030800, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Qingyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Runa","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanxi Agricultural University, Jinzhong 030800, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"software","vocabulary":"credit"}]},{"given":"Jie","family":"Hu","sequence":"additional","affiliation":[{"name":"Shanxi Agricultural University, Jinzhong 030800, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Jia","family":"Lyu","sequence":"additional","affiliation":[{"name":"Shanxi Agricultural University, Jinzhong 030800, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"given":"Zichen","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanxi Agricultural University, Jinzhong 030800, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8389-7732","authenticated-orcid":false,"given":"Zhilin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanxi Agricultural University, Jinzhong 030800, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1143-540X","authenticated-orcid":false,"given":"Mengshuai","family":"Feng","sequence":"additional","affiliation":[{"name":"Shanxi Agricultural University, Jinzhong 030800, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Feifan","family":"Wei","sequence":"additional","affiliation":[{"name":"Shanxi Agricultural University, Jinzhong 030800, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Vasconcelos, R.N., Franca Rocha, W.J.S., Costa, D.P., Duverger, S.G., Santana, M.M.M.d., Cambui, E.C.B., Ferreira-Ferreira, J., Oliveira, M., Barbosa, L.d.S., and Cordeiro, C.L. (2024). Fire Detection with Deep Learning: A Comprehensive Review. Land, 13.","DOI":"10.3390\/land13101696"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yuan, C., Liu, Z., and Zhang, Y. (2015). UAV-based forest fire detection and tracking using image processing techniques. Proceedings of the 2015 International Conference on Unmanned Aircraft Systems (ICUAS), Denver, CO, USA, 9\u201312 June 2015, IEEE.","DOI":"10.1109\/ICUAS.2015.7152345"},{"key":"ref_4","unstructured":"Valero, M.M., Rios, O., Planas, E., and Pastor, E. (2018). Extracting Active Fire Spread Parameters from Unmanned Aerial Vehicle Imagery. Remote Sens., 10."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_6","unstructured":"(2024, May 15). Ultralytics YOLO. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103910","DOI":"10.1016\/j.imavis.2020.103910","article-title":"Recent advances in small object detection based on deep learning: A review","volume":"97","author":"Tong","year":"2020","journal-title":"Image Vis. Comput."},{"key":"ref_8","first-page":"5998","article-title":"Attention is all you need","volume":"Volume 30","author":"Guyon","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21\u201326 July 2017, IEEE.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, T., Huang, J., Zhang, H., and Sun, Q. (2020). Visual Commonsense R-CNN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13\u201319 June 2020, IEEE.","DOI":"10.1109\/CVPR42600.2020.01077"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019). Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15\u201320 June 2019, IEEE.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_12","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2018). mixup: Beyond Empirical Risk Minimization. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pearl, J. (2009). Causality: Models, Reasoning, and Inference, Cambridge University Press. [2nd ed.].","DOI":"10.1017\/CBO9780511803161"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13\u201319 June 2020, IEEE.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_15","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_16","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 18\u201322 June 2023, IEEE.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hwang, S., Park, J., Kim, N., Choi, Y., and Kweon, I.S. (2015). Multispectral Pedestrian Detection: Benchmark Dataset and Baseline. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7\u201312 June 2015, IEEE.","DOI":"10.1109\/CVPR.2015.7298706"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.patcog.2018.08.005","article-title":"Illumination-aware faster R-CNN for robust multispectral pedestrian detection","volume":"85","author":"Li","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.patrec.2024.02.012","article-title":"CrossFormer: Cross-guided attention for multi-modal object detection","volume":"179","author":"Lee","year":"2024","journal-title":"Pattern Recognit. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9560","DOI":"10.1109\/TIP.2026.3728660","article-title":"Beyond Stepwise Modeling: Towards a Unified Contextual Reasoning Framework for Hyperspectral Video Object Tracking","volume":"35","author":"Chen","year":"2026","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yuan, Q., Zhong, B., Hu, X., Zhou, J., Zhao, X., Su, X., Tang, Y., and Zhang, L. (2026). CASS: A Context-Aware Memory Framework with Spectral-Spatial Modulation for UAV Multispectral Video Tracking. IEEE Trans. Circuits Syst. Video Technol., early access.","DOI":"10.1109\/TCSVT.2026.3708187"},{"key":"ref_23","first-page":"5631122","article-title":"STAR: A Unified Spatiotemporal Fusion Framework for Satellite Video Object Tracking","volume":"63","author":"Chen","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tang, K., Niu, Y., Huang, J., Shi, J., and Zhang, H. (2020). Unbiased Scene Graph Generation from Biased Training. arXiv.","DOI":"10.1109\/CVPR42600.2020.00377"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X.-S., and Wen, J.-R. (2021). Counterfactual VQA: A Cause-Effect Look at Language Bias. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20\u201325 June 2021, IEEE.","DOI":"10.1109\/CVPR46437.2021.01251"},{"key":"ref_26","unstructured":"Yue, Z., Zhang, H., Sun, Q., and Hua, X.-S. (2020). Interventional Few-Shot Learning. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","article-title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression","volume":"Volume 34","author":"Zheng","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18\u201322 June 2018, IEEE.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chino, D.Y.T., Avalhais, L.P.S., Rodrigues, J.F., and Traina, A.J.M. (2015). BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis. Proceedings of the 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images, Salvador, Brazil, 26\u201329 August 2015, IEEE.","DOI":"10.1109\/SIBGRAPI.2015.19"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108001","DOI":"10.1016\/j.comnet.2021.108001","article-title":"Aerial imagery pile burn detection using deep learning: The FLAME dataset","volume":"193","author":"Shamsoshoara","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Padilla, R., Netto, S.L., and da Silva, E.A.B. (2020). A Survey on Performance Metrics for Object-Detection Algorithms. Proceedings of the 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Niter\u00f3i, Brazil, 1\u20133 July 2020, IEEE.","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","article-title":"Microsoft COCO: Common Objects in Context","volume":"Volume 8693","author":"Fleet","year":"2014","journal-title":"Computer Vision\u2014ECCV 2014"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object Detection in 20 Years: A Survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e23127","DOI":"10.1016\/j.heliyon.2023.e23127","article-title":"Forest fire surveillance systems: A review of deep learning methods","volume":"10","author":"Saleh","year":"2024","journal-title":"Heliyon"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"108309","DOI":"10.1016\/j.sigpro.2021.108309","article-title":"A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms","volume":"190","author":"Bouguettaya","year":"2022","journal-title":"Signal Process."}],"container-title":["Plants"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/19\/2927\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T12:20:19Z","timestamp":1790252419000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/19\/2927"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":36,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["plants15192927"],"URL":"https:\/\/doi.org\/10.3390\/plants15192927","relation":{},"ISSN":["2223-7747"],"issn-type":[{"value":"2223-7747","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}