Leaf Classification for Sustainable Agriculture and In-Depth Species Analysis
Effective leaf classification is crucial for ecological research and agricultural management, though handling diverse leaf shapes and environmental variations can be difficult. This study introduces an innovative approach that combines advanced feature extraction techniques with optimization and cla...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10830505/ |
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| author | Sara Mumtaz Shabbab Algamdi Haifa F. Alhasson Dina Abdulaziz Alhammadi Ahmad Jalal Hui Liu |
| author_facet | Sara Mumtaz Shabbab Algamdi Haifa F. Alhasson Dina Abdulaziz Alhammadi Ahmad Jalal Hui Liu |
| author_sort | Sara Mumtaz |
| collection | DOAJ |
| description | Effective leaf classification is crucial for ecological research and agricultural management, though handling diverse leaf shapes and environmental variations can be difficult. This study introduces an innovative approach that combines advanced feature extraction techniques with optimization and classification strategies. We employ Scale Invariant feature transform (SIFT), wavelet transform, and particle gradient motion for feature extraction, followed by bee colony optimization to enhance the process. A Gaussian distribution-based classifier is subsequently utilized, achieving an accuracy of 92%, while a Random forest classifier applied to the Grapevine Leave Dataset resulted in an accuracy of 84.63%. This approach highlights the potential for improved plant identification and disease management, providing a reliable solution for both small and large-scale agricultural applications. The resilience of this framework guarantees adaptability across various datasets, making it a valuable asset for improving crop quality and sustainability. |
| format | Article |
| id | doaj-art-fe938895f8e248eeb3b17f97003dbc72 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fe938895f8e248eeb3b17f97003dbc722025-01-31T00:01:33ZengIEEEIEEE Access2169-35362025-01-0113170431705310.1109/ACCESS.2025.352688810830505Leaf Classification for Sustainable Agriculture and In-Depth Species AnalysisSara Mumtaz0Shabbab Algamdi1Haifa F. Alhasson2Dina Abdulaziz Alhammadi3Ahmad Jalal4https://orcid.org/0009-0000-8421-8477Hui Liu5https://orcid.org/0000-0002-6850-9570Department of Computing and AI, Air University, Islamabad, PakistanDepartment of Software Engineering, College of Computer Science and Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Computing and AI, Air University, Islamabad, PakistanCognitive Systems Laboratory, University of Bremen, Bremen, GermanyEffective leaf classification is crucial for ecological research and agricultural management, though handling diverse leaf shapes and environmental variations can be difficult. This study introduces an innovative approach that combines advanced feature extraction techniques with optimization and classification strategies. We employ Scale Invariant feature transform (SIFT), wavelet transform, and particle gradient motion for feature extraction, followed by bee colony optimization to enhance the process. A Gaussian distribution-based classifier is subsequently utilized, achieving an accuracy of 92%, while a Random forest classifier applied to the Grapevine Leave Dataset resulted in an accuracy of 84.63%. This approach highlights the potential for improved plant identification and disease management, providing a reliable solution for both small and large-scale agricultural applications. The resilience of this framework guarantees adaptability across various datasets, making it a valuable asset for improving crop quality and sustainability.https://ieeexplore.ieee.org/document/10830505/User experiencemachine learningobject recognitionhuman computer interactionuser-centered designartificial intelligence |
| spellingShingle | Sara Mumtaz Shabbab Algamdi Haifa F. Alhasson Dina Abdulaziz Alhammadi Ahmad Jalal Hui Liu Leaf Classification for Sustainable Agriculture and In-Depth Species Analysis IEEE Access User experience machine learning object recognition human computer interaction user-centered design artificial intelligence |
| title | Leaf Classification for Sustainable Agriculture and In-Depth Species Analysis |
| title_full | Leaf Classification for Sustainable Agriculture and In-Depth Species Analysis |
| title_fullStr | Leaf Classification for Sustainable Agriculture and In-Depth Species Analysis |
| title_full_unstemmed | Leaf Classification for Sustainable Agriculture and In-Depth Species Analysis |
| title_short | Leaf Classification for Sustainable Agriculture and In-Depth Species Analysis |
| title_sort | leaf classification for sustainable agriculture and in depth species analysis |
| topic | User experience machine learning object recognition human computer interaction user-centered design artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10830505/ |
| work_keys_str_mv | AT saramumtaz leafclassificationforsustainableagricultureandindepthspeciesanalysis AT shabbabalgamdi leafclassificationforsustainableagricultureandindepthspeciesanalysis AT haifafalhasson leafclassificationforsustainableagricultureandindepthspeciesanalysis AT dinaabdulazizalhammadi leafclassificationforsustainableagricultureandindepthspeciesanalysis AT ahmadjalal leafclassificationforsustainableagricultureandindepthspeciesanalysis AT huiliu leafclassificationforsustainableagricultureandindepthspeciesanalysis |