ADeepWeeD: An adaptive deep learning framework for weed species classification
Efficient weed management in agricultural fields is essential for attaining optimal crop yields and safeguarding global food security. Every year, farmers worldwide invest significant time, capital, and resources to combat yield losses, approximately USD 75.6 billion, due to weed infestations. Deep...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-12-01
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| Series: | Artificial Intelligence in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721725000492 |
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| author | Md Geaur Rahman Md Anisur Rahman Mohammad Zavid Parvez Md Anwarul Kaium Patwary Tofael Ahamed David A. Fleming-Muñoz Saad Aloteibi Mohammad Ali Moni, PhD |
| author_facet | Md Geaur Rahman Md Anisur Rahman Mohammad Zavid Parvez Md Anwarul Kaium Patwary Tofael Ahamed David A. Fleming-Muñoz Saad Aloteibi Mohammad Ali Moni, PhD |
| author_sort | Md Geaur Rahman |
| collection | DOAJ |
| description | Efficient weed management in agricultural fields is essential for attaining optimal crop yields and safeguarding global food security. Every year, farmers worldwide invest significant time, capital, and resources to combat yield losses, approximately USD 75.6 billion, due to weed infestations. Deep Learning (DL) methodologies have been recently implemented to revolutionise agricultural practices, particularly in weed detection and classification. Existing DL-based weed classification techniques, including VGG16 and ResNet50, initially construct a model by implementing the algorithm on a training dataset comprising weed species, subsequently employing the model to identify weed species acquired during training. Given the dynamic nature of crop fields, we argue that existing methods may exhibit suboptimal performance due to two key issues: (i) the unavailability of all training weed species initially, as these species emerge over time, resulting in a progressively expanding training dataset, and (ii) the constrained memory and computational capacity of the system utilised for model development, which hinders the retention of all weed species that manifest over an extended duration. To address the issues, this paper introduces a novel DL-based framework called ADeepWeeD for weed classification that facilitates adaptive (i.e. incremental) learning so that it can handle new weed species by keeping track of historical information. ADeepWeeD is evaluated using two criteria, namely F1-Score and classification accuracy, by comparing its performances against four non-incremental and two incremental state-of-the-art methods on three publicly available large datasets. Our experimental results demonstrate that ADeepWeeD outperforms existing techniques used in this study. We believe that our developed model could be used to develop an automation system for weed identification. The code of the proposed method is available on GitHub: https://github.com/grahman20/ADeepWeed. |
| format | Article |
| id | doaj-art-5a7e96f1ad36443fbbd07f93ff9715a9 |
| institution | Kabale University |
| issn | 2589-7217 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj-art-5a7e96f1ad36443fbbd07f93ff9715a92025-08-20T03:55:22ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172025-12-0115459060910.1016/j.aiia.2025.04.009ADeepWeeD: An adaptive deep learning framework for weed species classificationMd Geaur Rahman0Md Anisur Rahman1Mohammad Zavid Parvez2Md Anwarul Kaium Patwary3Tofael Ahamed4David A. Fleming-Muñoz5Saad Aloteibi6Mohammad Ali Moni, PhD7School of Computing, Mathematics and Engineering, Charles Sturt University, Port Macquarie, NSW 2444, Australia; Corresponding author.La Trobe Business School, La Trobe University, Melbourne, VIC 3086, AustraliaSchool of Computing, Mathematics and Engineering, Charles Sturt University, Port Macquarie, NSW 2444, AustraliaSchool of Physics, Maths and Computing, The University of Western Australia, Perth, WA 6009, AustraliaFaculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba Science City, 305-8572 Ibaraki, JapanLa Trobe Business School, La Trobe University, Melbourne, VIC 3086, AustraliaComputer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi ArabiaAI & Digital Health Technology, Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia; AI & Digital Health Technology, Rural Health Research Institute, Charles Sturt University, Orange, NSW 2800, Australia; School of IT, Washington University of Science and Technology, VA, USAEfficient weed management in agricultural fields is essential for attaining optimal crop yields and safeguarding global food security. Every year, farmers worldwide invest significant time, capital, and resources to combat yield losses, approximately USD 75.6 billion, due to weed infestations. Deep Learning (DL) methodologies have been recently implemented to revolutionise agricultural practices, particularly in weed detection and classification. Existing DL-based weed classification techniques, including VGG16 and ResNet50, initially construct a model by implementing the algorithm on a training dataset comprising weed species, subsequently employing the model to identify weed species acquired during training. Given the dynamic nature of crop fields, we argue that existing methods may exhibit suboptimal performance due to two key issues: (i) the unavailability of all training weed species initially, as these species emerge over time, resulting in a progressively expanding training dataset, and (ii) the constrained memory and computational capacity of the system utilised for model development, which hinders the retention of all weed species that manifest over an extended duration. To address the issues, this paper introduces a novel DL-based framework called ADeepWeeD for weed classification that facilitates adaptive (i.e. incremental) learning so that it can handle new weed species by keeping track of historical information. ADeepWeeD is evaluated using two criteria, namely F1-Score and classification accuracy, by comparing its performances against four non-incremental and two incremental state-of-the-art methods on three publicly available large datasets. Our experimental results demonstrate that ADeepWeeD outperforms existing techniques used in this study. We believe that our developed model could be used to develop an automation system for weed identification. The code of the proposed method is available on GitHub: https://github.com/grahman20/ADeepWeed.http://www.sciencedirect.com/science/article/pii/S2589721725000492Weed classificationAdaptive learningDeep learningArtificial intelligenceAgricultural automationPrecision agriculture |
| spellingShingle | Md Geaur Rahman Md Anisur Rahman Mohammad Zavid Parvez Md Anwarul Kaium Patwary Tofael Ahamed David A. Fleming-Muñoz Saad Aloteibi Mohammad Ali Moni, PhD ADeepWeeD: An adaptive deep learning framework for weed species classification Artificial Intelligence in Agriculture Weed classification Adaptive learning Deep learning Artificial intelligence Agricultural automation Precision agriculture |
| title | ADeepWeeD: An adaptive deep learning framework for weed species classification |
| title_full | ADeepWeeD: An adaptive deep learning framework for weed species classification |
| title_fullStr | ADeepWeeD: An adaptive deep learning framework for weed species classification |
| title_full_unstemmed | ADeepWeeD: An adaptive deep learning framework for weed species classification |
| title_short | ADeepWeeD: An adaptive deep learning framework for weed species classification |
| title_sort | adeepweed an adaptive deep learning framework for weed species classification |
| topic | Weed classification Adaptive learning Deep learning Artificial intelligence Agricultural automation Precision agriculture |
| url | http://www.sciencedirect.com/science/article/pii/S2589721725000492 |
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