{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T12:48:59Z","timestamp":1779886139454,"version":"3.53.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T00:00:00Z","timestamp":1769212800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"vor","delay-in-days":24,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Manipal Academy of Higher Education, Manipal"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The automated weed identification is necessary to enhance crop yield and the sustainable precision agriculture. The use of manual labour and chemical herbicides in traditional practices makes them very expensive, harmful to the environment, and causes more herbicide resistance. To overcome, this paper presents an AI-driven model AgroWeedX-Ensemble, which combines enhanced preprocessing, state-of-the-art segmentation, multi-scale feature extraction, feature selection, and efficient weed detection. An enhanced WeedNet-Adaptive Pre-Processing Optimizer (WN-APO) improves the quality of data by dynamically changing bilateral filtering, augmentation, and normalization to accommodate lighting changes and shadows. The proposed Attention-ASPP Enhanced Hybrid Dilated Network (AA-HDN) is based on the U-Net\u2009+\u2009\u2009+\u2009, attention, ASPP, and hybrid dilated convolutions to precisely differentiate between overlapping weed-crop areas. Multi-Scale Residual Spatial Feature Extractor (MS-RSFE) uses HOG, Gabor filters, and ResNet-50 with Feature Pyramid Networks to identify a variety of morphologies of weeds. The Parrot-Wheel Feature Selector (PWFS) is used to reduce feature redundancy by combining Parrot Optimizer and Binary Waterwheel Plant Optimization. Lastly, WeedAttnX-Net is a CNN-based model that adds RNN, Bi-GRU, attention layers, and the Bi-GRU-Attention to lower the false positives and enhance the reliability of the detection. The experimental findings show that AgroWeedX-Ensemble is highly accurate (0.99), precise (0.98), and robust in different field conditions with a lower false positives (0.02) and false negatives (0.009). The suggested system provides a scalable and useful solution to automated weed detection in precision agriculture.<\/jats:p>","DOI":"10.1007\/s44163-026-00853-9","type":"journal-article","created":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T02:16:47Z","timestamp":1769221007000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A unified framework with U-Net\u2009+\u2009\u2009+\u2009and CNN-RNN-BiGRU architectures for automated weed detection in precision agriculture using AI"],"prefix":"10.1007","volume":"6","author":[{"given":"Vijesh Kumar","family":"Patel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kumar","family":"Abhishek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B. M. Ahamed","family":"Shafeeq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,24]]},"reference":[{"issue":"4","key":"853_CR1","volume":"9","author":"HJ Beckie","year":"2020","unstructured":"Beckie HJ. Herbicide resistance in plants. Plants (Basel). 2020;9(4):435.","journal-title":"Plants (Basel)"},{"issue":"12","key":"853_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/app12125919","volume":"12","author":"M Altalak","year":"2022","unstructured":"Altalak M, Ammad uddin M, Alajmi A, Rizg A. Smart agriculture applications using deep learning technologies: a survey. Appl Sci. 2022;12(12):5919.","journal-title":"Appl Sci"},{"issue":"1","key":"853_CR3","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3390\/agronomy12010118","volume":"12","author":"A Monteiro","year":"2022","unstructured":"Monteiro A, Santos S. Sustainable approach to weed management: the role of precision weed management. Agronomy. 2022;12(1):118.","journal-title":"Agronomy"},{"key":"853_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108270","volume":"214","author":"F Visentin","year":"2023","unstructured":"Visentin F, Cremasco S, Sozzi M, Signorini L, Signorini M, Marinello F, et al. A mixed-autonomous robotic platform for intra-row and inter-row weed removal for precision agriculture. Comput Electron Agric. 2023;214:108270.","journal-title":"Comput Electron Agric"},{"key":"853_CR5","doi-asserted-by":"publisher","first-page":"3500","DOI":"10.1016\/j.matpr.2021.07.281","volume":"80","author":"AV Panchal","year":"2023","unstructured":"Panchal AV, Patel SC, Bagyalakshmi K, Kumar P, Khan IR, Soni M. Image-based plant diseases detection using deep learning. Mater Today Proc. 2023;80:3500\u20136.","journal-title":"Mater Today Proc"},{"key":"853_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106719","volume":"194","author":"A Picon","year":"2022","unstructured":"Picon A, San-Emeterio MG, Bereciartua-Perez A, Klukas C, Eggers T, Navarra-Mestre R. Deep learning-based segmentation of multiple species of weeds and corn crop using synthetic and real image datasets. Comput Electron Agric. 2022;194:106719.","journal-title":"Comput Electron Agric"},{"key":"853_CR7","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.biosystemseng.2021.01.014","volume":"204","author":"B Espejo-Garcia","year":"2021","unstructured":"Espejo-Garcia B, Mylonas N, Athanasakos L, Vali E, Fountas S. Combining generative adversarial networks and agricultural transfer learning for weeds identification. Biosyst Eng. 2021;204:79\u201389.","journal-title":"Biosyst Eng"},{"issue":"1","key":"853_CR8","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1109\/LRA.2017.2774979","volume":"3","author":"I Sa","year":"2017","unstructured":"Sa I, Chen Z, Popovi\u0107 M, Khanna R, Liebisch F, Nieto J, et al. weednet: Dense semantic weed classification using multispectral images and mav for smart farming. IEEE Robot Autom Lett. 2017;3(1):588\u201395.","journal-title":"IEEE Robot Autom Lett"},{"issue":"2","key":"853_CR9","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1002\/rob.21938","volume":"37","author":"X Wu","year":"2020","unstructured":"Wu X, Aravecchia S, Lottes P, Stachniss C, Pradalier C. Robotic weed control using automated weed and crop classification. J Field Robotics. 2020;37(2):322\u201340.","journal-title":"J Field Robotics"},{"issue":"17","key":"853_CR10","doi-asserted-by":"publisher","first-page":"3517","DOI":"10.3390\/rs13173517","volume":"13","author":"R Xu","year":"2021","unstructured":"Xu R, Li C, Bernardes S. Development and testing of a UAV-based multi-sensor system for plant phenotyping and precision agriculture. Remote Sens. 2021;13(17):3517.","journal-title":"Remote Sens"},{"key":"853_CR11","first-page":"276","volume":"6","author":"N Kundu","year":"2022","unstructured":"Kundu N, Rani G, Dhaka VS, Gupta K, Nayaka SC, Vocaturo E, et al. Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning. Artif Intell Agric. 2022;6:276\u201391.","journal-title":"Artif Intell Agric"},{"issue":"1","key":"853_CR12","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1002\/rob.21901","volume":"37","author":"P Lottes","year":"2020","unstructured":"Lottes P, Behley J, Chebrolu N, Milioto A, Stachniss C. Robust joint stem detection and crop-weed classification using image sequences for plant-specific treatment in precision farming. J Field Robotics. 2020;37(1):20\u201334.","journal-title":"J Field Robotics"},{"issue":"1","key":"853_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1):1\u201348.","journal-title":"J Big Data"},{"issue":"3","key":"853_CR14","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1002\/agj2.21353","volume":"116","author":"M Gardezi","year":"2024","unstructured":"Gardezi M, Joshi B, Rizzo DM, Ryan M, Prutzer E, Brugler S, et al. Artificial intelligence in farming: challenges and opportunities for building trust. Agron J. 2024;116(3):1217\u201328.","journal-title":"Agron J"},{"issue":"11","key":"853_CR15","doi-asserted-by":"publisher","first-page":"8562","DOI":"10.3390\/su15118562","volume":"15","author":"FTD Silva","year":"2023","unstructured":"Silva FTD, Baierle IC, Correa RGDF, Sellitto MA, Peres FAP, Kipper LM. Open innovation in agribusiness: barriers and challenges in the transition to agriculture 4.0. Sustainability. 2023;15(11):8562.","journal-title":"Sustainability"},{"issue":"15","key":"853_CR16","doi-asserted-by":"publisher","DOI":"10.3390\/app13158840","volume":"13","author":"SIU Haq","year":"2023","unstructured":"Haq SIU, Tahir MN, Lan Y. Weed detection in wheat crops using image analysis and artificial intelligence (AI). Appl Sci. 2023;13(15):8840.","journal-title":"Appl Sci"},{"issue":"2","key":"853_CR17","doi-asserted-by":"publisher","first-page":"155","DOI":"10.47852\/bonviewJCCE2202174","volume":"2","author":"S Sharma","year":"2023","unstructured":"Sharma S, Verma K, Hardaha P. Implementation of artificial intelligence in agriculture. J Computat Cognit Eng. 2023;2(2):155\u201362.","journal-title":"J Computat Cognit Eng"},{"key":"853_CR18","first-page":"47","volume":"6","author":"A Subeesh","year":"2022","unstructured":"Subeesh A, Bhole S, Singh K, Chandel NS, Rajwade YA, Rao KVR, et al. Deep convolutional neural network models for weed detection in polyhouse grown bell peppers. Artific Intell Agricul. 2022;6:47\u201354.","journal-title":"Artific Intell Agricul"},{"issue":"5","key":"853_CR19","doi-asserted-by":"publisher","first-page":"387","DOI":"10.3390\/agriculture11050387","volume":"11","author":"N Islam","year":"2021","unstructured":"Islam N, Rashid MM, Wibowo S, Xu CY, Morshed A, Wasimi SA, et al. Early weed detection using image processing and machine learning techniques in an Australian chilli farm. Agriculture. 2021;11(5):387.","journal-title":"Agriculture"},{"key":"853_CR20","doi-asserted-by":"crossref","unstructured":"Parasuraman, K., Anandan, U. and Anbarasan, A., 2021, February. IoT based smart agriculture automation in artificial intelligence. In: 2021 third international conference on intelligent communication technologies and virtual mobile networks (ICICV) (pp. 420\u2013427). IEEE.","DOI":"10.1109\/ICICV50876.2021.9388578"},{"issue":"12","key":"853_CR21","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy12122953","volume":"12","author":"JM L\u00f3pez-Correa","year":"2022","unstructured":"L\u00f3pez-Correa JM, Moreno H, Ribeiro A, And\u00fajar D. Intelligent weed management based on object detection neural networks in tomato crops. Agronomy. 2022;12(12):2953.","journal-title":"Agronomy"},{"issue":"3","key":"853_CR22","first-page":"5001","volume":"13","author":"P Kanade","year":"2021","unstructured":"Kanade P, Akhtar M, David F, Kanade S. Agricultural mobile robots in weed management and control. Int J Adv Network Appl. 2021;13(3):5001\u20136.","journal-title":"Int J Adv Network Appl"},{"key":"853_CR23","doi-asserted-by":"crossref","unstructured":"Patel, D., Gandhi, M., Shankaranarayanan, H. and Darji, A.D., 2022. Design of an Autonomous Agriculture Robot for Real-Time Weed Detection Using CNN. In Advances in VLSI and Embedded Systems: Select Proceedings of AVES 2021. Singapore: Springer Nature Singapore. (pp. 141\u2013161).","DOI":"10.1007\/978-981-19-6780-1_13"},{"issue":"11","key":"853_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture12111838","volume":"12","author":"B Costello","year":"2022","unstructured":"Costello B, Osunkoya OO, Sandino J, Marinic W, Trotter P, Shi B, et al. Detection of Parthenium weed (Parthenium hysterophorus L.) and its growth stages using artificial intelligence. Agriculture. 2022;12(11):1838.","journal-title":"Agriculture"},{"issue":"6","key":"853_CR25","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1007\/s11119-021-09808-9","volume":"22","author":"S Khan","year":"2021","unstructured":"Khan S, Tufail M, Khan MT, Khan ZA, Anwar S. Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer. Precis Agric. 2021;22(6):1711\u201327.","journal-title":"Precis Agric"},{"key":"853_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2025.100883","volume":"11","author":"T Jin","year":"2025","unstructured":"Jin T, Liang K, Lu M, Zhao Y, Xu Y. WeedsSORT: a weed tracking-by-detection framework for laser weeding applications within precision agriculture. Smart Agric Technol. 2025;11:100883.","journal-title":"Smart Agric Technol"},{"key":"853_CR27","doi-asserted-by":"crossref","unstructured":"Borah, S., Anand, A., Sundaravadivel, P., Fletcher, R., & Reddy, K. (2025). Unmanned Aerial Vehicle Solutions for Weed Detection in Precision Agriculture.\u00a0IEEE Access.","DOI":"10.1109\/ACCESS.2025.3575601"},{"issue":"3","key":"853_CR28","doi-asserted-by":"publisher","first-page":"2759","DOI":"10.32604\/csse.2023.027647","volume":"44","author":"R Punithavathi","year":"2023","unstructured":"Punithavathi R, Rani ADC, Sughashini KR, Kurangi C, Nirmala M, Ahmed HFT, et al. Computer vision and deep learning-enabled weed detection model for precision agriculture. Comput Syst Sci Eng. 2023;44(3):2759\u201374.","journal-title":"Comput Syst Sci Eng"},{"key":"853_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/access.2025.3538937","author":"S Sandosh","year":"2025","unstructured":"Sandosh S, Ajay A, Saji A, Agarwal H. An explainable deep learning framework for Sorghum weed classification using multi-scale feature enhanced DenseNet. IEEE Access. 2025. https:\/\/doi.org\/10.1109\/access.2025.3538937.","journal-title":"IEEE Access"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-026-00853-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-00853-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-026-00853-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T11:58:17Z","timestamp":1771329497000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-026-00853-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,24]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["853"],"URL":"https:\/\/doi.org\/10.1007\/s44163-026-00853-9","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,24]]},"assertion":[{"value":"4 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"No.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Dual-publication"}},{"value":"Yes.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Authorship"}},{"value":"Yes.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Open Access"}},{"value":"No.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Third-party material"}},{"value":"The authors declare no competing interests.","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"144"}}