{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T23:54:18Z","timestamp":1785974058014,"version":"3.56.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Geoinformatica"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10707-026-00564-4","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:33:12Z","timestamp":1773214392000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multispectral image fusion and attention-driven deep learning for precision weed segmentation and classification in UAV-based agricultural monitoring"],"prefix":"10.1007","volume":"30","author":[{"given":"Narra","family":"Dhanalakshmi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lam","family":"Padma sree","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bai B.","family":"Mathura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"564_CR1","doi-asserted-by":"publisher","first-page":"109412","DOI":"10.1016\/j.compag.2024.109412","volume":"226","author":"C Parra-L\u00f3pez","year":"2024","unstructured":"Parra-L\u00f3pez C, Ben Abdallah S, Garcia-Garcia G, Hassoun A, S\u00e1nchez-Zamora P, Trollman H, Jagtap S, Carmona-Torres C (2024) Integrating Digital Technologies in agriculture for climate change adaptation and mitigation: State of the Art and Future Perspectives. Comput Electron Agric 226:109412","journal-title":"Comput Electron Agric"},{"key":"564_CR2","doi-asserted-by":"crossref","unstructured":"Sabir RM, Mehmood K, Sarwar A et al (2024) Remote sensing and precision agriculture: a sustainable future. transforming agricultural management for a sustainable future: climate change and machine learning perspectives 75\u2013103","DOI":"10.1007\/978-3-031-63430-7_4"},{"key":"564_CR3","doi-asserted-by":"publisher","first-page":"44786","DOI":"10.1109\/ACCESS.2024.3380830","volume":"12","author":"IM Mehedi","year":"2024","unstructured":"Mehedi IM, Hanif MS, Bilal M, Vellingiri MT, Palaniswamy T (2024) Remote Sensing and decision support system applications in Precision Agriculture: Challenges and Possibilities. IEEE Access 12:44786\u201344798","journal-title":"IEEE Access"},{"key":"564_CR4","doi-asserted-by":"crossref","unstructured":"Farid HU, Mustafa B, Khan ZM, Anjum MN, Ahmad I, Mubeen M, Shahzad H (2023) An overview of precision agricultural technologies for crop yield enhancement and environmental sustainability. Clim Change Impacts Agric 239\u2013257","DOI":"10.1007\/978-3-031-26692-8_14"},{"key":"564_CR5","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1007\/s13762-021-03801-5","volume":"20","author":"M Awais","year":"2022","unstructured":"Awais M, Li W, Cheema MJ et al (2022) UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for Digital Agriculture Practices: A meta-review. Int J Environ Sci Technol 20:1135\u20131152","journal-title":"Int J Environ Sci Technol"},{"key":"564_CR6","doi-asserted-by":"crossref","unstructured":"Tahir MN, Lan Y, Zhang Y, Wenjiang H, Wang Y, Syed Muhammad Zaigham Abbas Naqvi (2023) Application of unmanned aerial vehicles in Precision Agriculture. Precision Agric 55\u201370","DOI":"10.1016\/B978-0-443-18953-1.00001-5"},{"key":"564_CR7","doi-asserted-by":"crossref","unstructured":"Dhaked MK, Saryam M, Tomar DS, Bhargava M (2025) Strategies and challenges of remote sensing, GIS, and IOT Tools for Disease Control operations for future needs of Indian farmers. Smart Agric 143\u2013165","DOI":"10.1007\/978-981-97-9800-1_8"},{"key":"564_CR8","doi-asserted-by":"publisher","first-page":"6945","DOI":"10.1109\/JSTARS.2024.3377104","volume":"17","author":"M Barjaktarovic","year":"2024","unstructured":"Barjaktarovic M, Santoni M, Bruzzone L (2024) Design and verification of a low-cost multispectral camera for Precision Agriculture Application. IEEE J Sel Top Appl Earth Observ Remote Sens 17:6945\u20136957","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"key":"564_CR9","doi-asserted-by":"crossref","unstructured":"Surendran U, Nagakumar KCh, Samuel MP (2024) Remote Sensing in precision agriculture. Digit Agric 201\u2013223","DOI":"10.1007\/978-3-031-43548-5_7"},{"key":"564_CR10","doi-asserted-by":"publisher","first-page":"9511","DOI":"10.1007\/s00521-022-07104-9","volume":"34","author":"A Bouguettaya","year":"2022","unstructured":"Bouguettaya A, Zarzour H, Kechida A, Taberkit AM (2022) Deep learning techniques to classify agricultural crops through UAV imagery: A Review. Neural Comput Appl 34:9511\u20139536","journal-title":"Neural Comput Appl"},{"key":"564_CR11","doi-asserted-by":"publisher","first-page":"101405","DOI":"10.1016\/j.atech.2025.101405","volume":"11","author":"F Garibaldi-M\u00e1rquez","year":"2025","unstructured":"Garibaldi-M\u00e1rquez F, Valent\u00edn-Coronado LM, D\u00edaz-Ponce A, Serv\u00edn-Palestina M, Garc\u00eda-Hern\u00e1ndez RV, Ramos-Cant\u00fa L (2025) Advances on deep learning for proximal image-based weed recognition and control under authentic farmlands: a state-of-the-art review. Smart Agric Technol 11:101405","journal-title":"Smart Agric Technol"},{"key":"564_CR12","doi-asserted-by":"crossref","unstructured":"Garibaldi-M\u00e1rquez F, Flores G, Valent\u00edn-Coronado LM (2025) Leveraging deep semantic segmentation for assisted weed detection. J Agric Eng 56","DOI":"10.4081\/jae.2025.1741"},{"key":"564_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10707-025-00561-z","volume":"30","author":"M Adamiak","year":"2026","unstructured":"Adamiak M, B\u0119dkowski K, Nalej M, O\u017cadowicz P, Pietruk J, W\u00f3jcik S (2026) Bi-temporal change detection for topographic map updates using panoptic segmentation of VNIR orthophotos and LiDAR data. GeoInformatica 30:1\u201341","journal-title":"GeoInformatica"},{"key":"564_CR14","doi-asserted-by":"publisher","first-page":"26973","DOI":"10.1109\/ACCESS.2025.3538937","volume":"13","author":"A Ajay","year":"2025","unstructured":"Ajay A, Sandosh S, Saji A, Agarwal H (2025) An explainable deep learning framework for sorghum weed classification using multi-scale feature enhanced DenseNet. IEEE Access 13:26973\u201326990","journal-title":"IEEE Access"},{"key":"564_CR15","first-page":"101931","volume":"21","author":"M Aqil","year":"2025","unstructured":"Aqil M, Azrai M, Efendi R et al (2025) Deep learning based stacking ensembles for tropical sorghum classification. J Agric Food Res 21:101931","journal-title":"J Agric Food Res"},{"key":"564_CR16","doi-asserted-by":"publisher","first-page":"5749","DOI":"10.1109\/JSTARS.2025.3536175","volume":"18","author":"AL Machidon","year":"2025","unstructured":"Machidon AL, Kra\u0161ovec A, Pejovi\u0107 V, Machidon OM (2025) Squeezeslimu-net: An adaptive and efficient segmentation architecture for real-time UAV weed detection. IEEE J Sel Top Appl Earth Observ Remote Sens 18:5749\u20135764","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"key":"564_CR17","doi-asserted-by":"publisher","first-page":"34913","DOI":"10.1007\/s11042-023-16739-2","volume":"83","author":"M Madanan","year":"2024","unstructured":"Madanan M, Muthukumaran N, Tiwari S, Vijay A, Saha I (2024) RSA based improved Yolov3 Network for segmentation and detection of weed species. Multimed Tools Appl 83:34913\u201334942","journal-title":"Multimed Tools Appl"},{"key":"564_CR18","doi-asserted-by":"publisher","first-page":"100465","DOI":"10.1016\/j.bdr.2024.100465","volume":"36","author":"MA Bhatti","year":"2024","unstructured":"Bhatti MA, Syam MS, Chen H, Hu Y, Keung LW, Zeeshan Z, Ali YA, Sarhan N (2024) Utilizing convolutional neural networks (CNN) and U-Net architecture for precise crop and weed segmentation in agricultural imagery: A deep learning approach. Big Data Res 36:100465","journal-title":"Big Data Res"},{"key":"564_CR19","doi-asserted-by":"publisher","first-page":"71982","DOI":"10.1109\/ACCESS.2024.3402213","volume":"12","author":"V Singh","year":"2024","unstructured":"Singh V, Singh D, Kumar H (2024) Efficient application of deep neural networks for identifying small and multiple weed patches using drone images. IEEE Access 12:71982\u201371996","journal-title":"IEEE Access"},{"key":"564_CR20","doi-asserted-by":"publisher","first-page":"107956","DOI":"10.1016\/j.compag.2023.107956","volume":"211","author":"HM Sahin","year":"2023","unstructured":"Sahin HM, Miftahushudur T, Grieve B, Yin H (2023) Segmentation of weeds and crops using multispectral imaging and CRF-enhanced U-net. Comput Electron Agric 211:107956","journal-title":"Comput Electron Agric"},{"key":"564_CR21","doi-asserted-by":"publisher","first-page":"100142","DOI":"10.1016\/j.atech.2022.100142","volume":"4","author":"SI Moazzam","year":"2023","unstructured":"Moazzam SI, Khan US, Qureshi WS, Nawaz T, Kunwar F (2023) Towards automated weed detection through two-stage semantic segmentation of tobacco and weed pixels in aerial imagery. Smart Agric Technol 4:100142","journal-title":"Smart Agric Technol"},{"key":"564_CR22","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s13007-023-01060-8","volume":"19","author":"N Genze","year":"2023","unstructured":"Genze N, Wirth M, Schreiner C, Ajekwe R, Grieb M, Grimm DG (2023) Improved weed segmentation in UAV imagery of sorghum fields with a combined deblurring segmentation model. Plant Methods 19:87","journal-title":"Plant Methods"},{"key":"564_CR23","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1186\/s13007-024-01232-0","volume":"20","author":"Z Guo","year":"2024","unstructured":"Guo Z, Cai D, Zhou Y, Xu T, Yu F (2024) Identifying rice field weeds from unmanned aerial vehicle remote sensing imagery using Deep Learning. Plant Methods 20:105","journal-title":"Plant Methods"},{"key":"564_CR24","doi-asserted-by":"publisher","first-page":"103114","DOI":"10.1016\/j.mex.2024.103114","volume":"14","author":"S Rana","year":"2025","unstructured":"Rana S, Gerbino S, Carillo P (2025) Study of spectral overlap and heterogeneity in agriculture based on soft classification techniques. MethodsX 14:103114","journal-title":"MethodsX"},{"key":"564_CR25","doi-asserted-by":"crossref","unstructured":"Rana S, Gerbino S, Akbari Sekehravani E, Russo MB, Carillo P (2024) Crop growth analysis using automatic annotations and transfer learning in multi-date aerial images and ortho-mosaics. Agronomy 14:2052","DOI":"10.3390\/agronomy14092052"},{"key":"564_CR26","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.3390\/agronomy11071435","volume":"11","author":"NN Che\u2019Ya","year":"2021","unstructured":"Che\u2019Ya NN, Dunwoody E, Gupta M (2021) Assessment of Weed Classification using hyperspectral reflectance and optimal multispectral UAV imagery. Agronomy 11:1435","journal-title":"Agronomy"},{"key":"564_CR27","doi-asserted-by":"publisher","first-page":"2419","DOI":"10.1109\/TIP.2009.2028250","volume":"18","author":"A Beck","year":"2009","unstructured":"Beck A, Teboulle M (2009) Fast gradient-based algorithms for constrained total variation image denoising and Deblurring problems. IEEE Trans Image Process 18:2419\u20132434","journal-title":"IEEE Trans Image Process"},{"key":"564_CR28","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1109\/TSA.2005.860851","volume":"14","author":"J Chen","year":"2006","unstructured":"Chen J, Benesty J, Yiteng Huang, Doclo S (2006) New insights into the noise reduction wiener filter. IEEE Trans Audio Speech Lang Process 14:1218\u20131234","journal-title":"IEEE Trans Audio Speech Lang Process"},{"key":"564_CR29","doi-asserted-by":"publisher","first-page":"127828","DOI":"10.1016\/j.neucom.2024.127828","volume":"595","author":"H Sun","year":"2024","unstructured":"Sun H, Wang Y, Wang X, Zhang B, Xin Y, Zhang B, Cao X, Ding E, Han S (2024) Maformer: A Transformer network with multi-scale attention fusion for visual recognition. Neurocomput 595:127828","journal-title":"Neurocomput"},{"key":"564_CR30","doi-asserted-by":"publisher","first-page":"3579","DOI":"10.1007\/s13042-024-02110-w","volume":"15","author":"I Pacal","year":"2024","unstructured":"Pacal I (2024) A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images. Int J Mach Learn Cybern 15:3579\u20133597","journal-title":"Int J Mach Learn Cybern"},{"key":"564_CR31","doi-asserted-by":"publisher","first-page":"1352935","DOI":"10.3389\/fpls.2024.1352935","volume":"15","author":"WM Elmessery","year":"2024","unstructured":"Elmessery WM, Maklakov DV, El-Messery TM, Baranenko DA, Guti\u00e9rrez J, Shams MY, El-Hafeez TA, Elsayed S, Alhag SK, Moghanm FS, Mulyukin MA (2024) Semantic segmentation of microbial alterations based on SegFormer. Front Plant Sci 15:1352935","journal-title":"Front Plant Sci"},{"key":"564_CR32","doi-asserted-by":"publisher","first-page":"11491","DOI":"10.1007\/s11042-020-10184-1","volume":"80","author":"A Khare","year":"2021","unstructured":"Khare A, Khare M, Srivastava R (2021) Shearlet transform based technique for image fusion using median fusion rule. Multimed Tools Appl 80:11491\u201311522","journal-title":"Multimed Tools Appl"},{"key":"564_CR33","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/TNNLS.2012.2226471","volume":"24","author":"R He","year":"2012","unstructured":"He R, Zheng W-S, Hu B-G, Xiang-Wei Kong (2012) Two-stage nonnegative sparse representation for large-scale face recognition. IEEE Trans Neural Networks Learn Syst 24:35\u201346","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"564_CR34","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.neunet.2017.07.017","volume":"95","author":"A Sharma","year":"2017","unstructured":"Sharma A, Liu X, Yang X, Shi D (2017) A patch-based convolutional neural network for Remote Sensing Image Classification. Neural Netw 95:19\u201328","journal-title":"Neural Netw"},{"key":"564_CR35","unstructured":"Barbero F, Cristian B, de Oc\u00e1riz Borde HS, Lio P (2022) Sheaf attention networks. In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations"},{"key":"564_CR36","doi-asserted-by":"crossref","unstructured":"Cinar N, Ozcan A, Kaya M (2022) A hybrid\u00a0DenseNet121-UNET model for Brain tumor segmentation from Mr Images. Biomed Signal Process Control 76:103647","DOI":"10.1016\/j.bspc.2022.103647"},{"issue":"1","key":"564_CR37","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3390\/e25010171","volume":"25","author":"JS Pan","year":"2023","unstructured":"Pan JS, Zhang SQ, Chu SC, Yang HM, Yan B (2023) Willow catkin optimization algorithm applied in the tdoa-fdoa joint location problem. Entropy 25(1):171","journal-title":"Entropy"},{"key":"564_CR38","first-page":"103","volume":"7","author":"A Syed","year":"2025","unstructured":"Syed A, Chen B, Abbasi AA, Butt SA, Fang X (2025) MSEA-net: Multi-scale and edge-aware network for weed segmentation. AgriEng 7:103","journal-title":"AgriEng"},{"key":"564_CR39","doi-asserted-by":"publisher","first-page":"106721","DOI":"10.1016\/j.cropro.2024.106721","volume":"182","author":"GA Mes\u00edas-Ruiz","year":"2024","unstructured":"Mes\u00edas-Ruiz GA, Borra-Serrano I, Pe\u00f1a JM, de Castro AI, Fern\u00e1ndez-Quintanilla C, Dorado J (2024) Weed species classification with UAV imagery and standard CNN models: Assessing the frontiers of training and Inference Phases. Crop Protect 182:106721","journal-title":"Crop Protect"},{"key":"564_CR40","doi-asserted-by":"publisher","first-page":"107075","DOI":"10.1016\/j.cropro.2024.107075","volume":"190","author":"F Garibaldi-M\u00e1rquez","year":"2025","unstructured":"Garibaldi-M\u00e1rquez F, Mart\u00ednez-Barba DA, Monta\u00f1ez-Franco LE, Flores G, Valent\u00edn-Coronado LM (2025) Enhancing site-specific weed detection using deep learning transformer architectures. Crop Protect 190:107075","journal-title":"Crop Protect"},{"key":"564_CR41","doi-asserted-by":"publisher","first-page":"1449514","DOI":"10.3389\/fpls.2024.1449514","volume":"15","author":"Y Li","year":"2025","unstructured":"Li Y, Guo R, Li R et al (2025) An improved U-net and attention mechanism-based model for sugar beet and weed segmentation. Front Plant Sci 15:1449514","journal-title":"Front Plant Sci"},{"key":"564_CR42","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1017\/wet.2022.46","volume":"36","author":"J Yang","year":"2022","unstructured":"Yang J, Bagavathiannan M, Wang Y, Chen Y, Yu J (2022) A comparative evaluation of convolutional neural networks, training image sizes, and deep learning optimizers for weed detection in alfalfa. Weed Technol 36:512\u2013522","journal-title":"Weed Technol"},{"key":"564_CR43","doi-asserted-by":"crossref","unstructured":"Kaur G, Bharany S, Elkamchouchi DH, Kim S (2025) Optimized ensemble learning for semantic segmentation of satellite imagery using DeepLabV3 + and UNet with PSO and cross-dataset evaluation. IEEE Access","DOI":"10.1109\/ACCESS.2025.3602922"},{"key":"564_CR44","doi-asserted-by":"publisher","first-page":"5888","DOI":"10.1038\/s41598-025-89961-7","volume":"15","author":"S Agarwal","year":"2025","unstructured":"Agarwal S, Dohare AK, Saxena P, Singh J, Singh I, Sahu UK (2025) HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification. Sci Rep 15:5888","journal-title":"Sci Rep"}],"container-title":["GeoInformatica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-026-00564-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10707-026-00564-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10707-026-00564-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T02:57:21Z","timestamp":1782097041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10707-026-00564-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,11]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["564"],"URL":"https:\/\/doi.org\/10.1007\/s10707-026-00564-4","relation":{},"ISSN":["1384-6175","1573-7624"],"issn-type":[{"value":"1384-6175","type":"print"},{"value":"1573-7624","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,11]]},"assertion":[{"value":"25 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All applicable institutional and\/or national guidelines for the care and use of animals were followed.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"For this type of analysis formal consent is not needed.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"10"}}