ItemConference Paper

Enhancing Weed Recognition in Cotton Fields Through Advanced Imaging and Learning Techniques

Title
Enhancing Weed Recognition in Cotton Fields Through Advanced Imaging and Learning Techniques
Creator
Singh, Jagendra; Shaik, Nazeer; Sahu, Dinesh Prasad; Tiwari, Mohit; Haque, Mustafizul; Upreti, Kamal
Description
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.
Source
Lecture Notes in Networks and Systems;Volume;1181;pp.467-478
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Advanced imaging; Cotton fields; Machine learning; Weed management; Weed recognition
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981978860-6;
Format
online
Language
English
Type
Conference paper

Citation

Singh, Jagendra; Shaik, Nazeer; Sahu, Dinesh Prasad; Tiwari, Mohit; Haque, Mustafizul; Upreti, Kamal, “Enhancing Weed Recognition in Cotton Fields Through Advanced Imaging and Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25675.

Output Formats