Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Jagendra; Shaik, Nazeer; Sahu, Dinesh Prasad; Tiwari, Mohit; Haque, Mustafizul; Upreti, Kamal Title A name given to the resource Enhancing Weed Recognition in Cotton Fields Through Advanced Imaging and Learning Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1181;pp.467-478 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-8861-3_39" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-8861-3_39</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000719078?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000719078?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Shaik N., Department of Computer Science and Engineering, Srinivasa Ramanujan Institute of TechnologyAutonomous, Anantapur, India; Sahu D.P., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Tiwari M., Department of Computer Science and Engineering, Bharati Vidyapeeths College of Engineering, Delhi, India; Haque M., Dr. D. Y. Patil Vidyapeeths Centre for Online Learning, Dr. D. Y. Patil Vidyapeeth, Pune, India; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Ghaziabad, India Description An account of the resource This research investigates the efficacy of weed recognition models in cotton fields through advanced imaging and machine learning techniques. Utilizing 10 trials, the models, namely K-NN and GBM, were evaluated across multiple performance metrics. Results reveal that GBM consistently outperformed K-NN in accuracy, precision, recall, and F1 score, with average values of 0.88, 0.89, 0.86, and 0.88, respectively, compared to K-NN's averages of 0.85, 0.87, 0.82, and 0.85. Moreover, GBM exhibited higher AUC values (0.94) than K-NN (0.92) in ROC curve analysis, indicating superior discrimination ability. Additionally, k-fold cross-validation demonstrated GBM's higher mean accuracy (0.89) and F1 score (0.88) compared to K-NN (mean accuracy: 0.86, mean F1 score: 0.85). Additionally, integrating temporal data analysis could improve the models ability to detect weed growth patterns over time. Real-time monitoring capabilities and automated decision-making systems could streamline weed management practices in agricultural settings. Furthermore, expanding the study to encompass diverse geographical regions and crop types would provide valuable insights into the generalizability and robustness of the developed models. Overall, continued research in this domain holds the potential to revolutionize weed management strategies and contribute to sustainable agriculture practices. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Advanced imaging; Cotton fields; Machine learning; Weed management; Weed recognition Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981978860-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online