Comparative analysis of image mosaicing techniques for aerial agriculture field imaging
UAVs that are outfitted with advanced image sensors offer a flexible and cost-efficient means of obtaining cohesive, inclusive, and precise visual depictions of regions. The obtained visual data, however, frequently comprises fragmented data as a result of the restricted perspective of individual im...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2025.2507744 |
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| author | Maria John S. Santhanalakshmi J Amudha Jianfeng Zhou |
| author_facet | Maria John S. Santhanalakshmi J Amudha Jianfeng Zhou |
| author_sort | Maria John |
| collection | DOAJ |
| description | UAVs that are outfitted with advanced image sensors offer a flexible and cost-efficient means of obtaining cohesive, inclusive, and precise visual depictions of regions. The obtained visual data, however, frequently comprises fragmented data as a result of the restricted perspective of individual images. To address this constraint, image mosaicing techniques are utilized to seamlessly merge various images, resulting in a thorough and uninterrupted depiction of the agricultural environment. The objective of this work is to evaluate and compare image mosaicing techniques, focusing primarily on agricultural datasets. It uncovers and exposes the constraints that exist in existing algorithms. The dataset of maize crop acquired from Phantom drones in the farms of Centralia, Missouri, USA, is utilized for this work. The Aerial images are used to evaluate distinctive feature-based and direct pixel-based mosaicing methods. The research analyzes four performance metrics, which consist of the Structural Similarity Index (SSIM) and Root Mean Square (RMS) error, together with Standard Deviation (SD) and CPU computational time. Analysis of Variance was conducted for the texture features of the generated mosaics. Several experimental findings show how the accuracy-performance-visual quality relationship affects the selection of suitable methods for real-time agricultural dataset. The research findings provide knowledge to select the best mosaicing solutions that will benefit crop health assessment and yield estimation applications. |
| format | Article |
| id | doaj-art-8ff0596966494d6d90fc2d3fa7a8369d |
| institution | DOAJ |
| issn | 2279-7254 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-8ff0596966494d6d90fc2d3fa7a8369d2025-08-20T03:13:32ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542025-12-0158110.1080/22797254.2025.2507744Comparative analysis of image mosaicing techniques for aerial agriculture field imagingMaria John0S. Santhanalakshmi1J Amudha2Jianfeng Zhou3Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidhyapeetham, Bengaluru, IndiaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidhyapeetham, Bengaluru, IndiaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidhyapeetham, Bengaluru, IndiaDivision of Plant Science and Technology, University of Missouri, Columbia, MO, USAUAVs that are outfitted with advanced image sensors offer a flexible and cost-efficient means of obtaining cohesive, inclusive, and precise visual depictions of regions. The obtained visual data, however, frequently comprises fragmented data as a result of the restricted perspective of individual images. To address this constraint, image mosaicing techniques are utilized to seamlessly merge various images, resulting in a thorough and uninterrupted depiction of the agricultural environment. The objective of this work is to evaluate and compare image mosaicing techniques, focusing primarily on agricultural datasets. It uncovers and exposes the constraints that exist in existing algorithms. The dataset of maize crop acquired from Phantom drones in the farms of Centralia, Missouri, USA, is utilized for this work. The Aerial images are used to evaluate distinctive feature-based and direct pixel-based mosaicing methods. The research analyzes four performance metrics, which consist of the Structural Similarity Index (SSIM) and Root Mean Square (RMS) error, together with Standard Deviation (SD) and CPU computational time. Analysis of Variance was conducted for the texture features of the generated mosaics. Several experimental findings show how the accuracy-performance-visual quality relationship affects the selection of suitable methods for real-time agricultural dataset. The research findings provide knowledge to select the best mosaicing solutions that will benefit crop health assessment and yield estimation applications.https://www.tandfonline.com/doi/10.1080/22797254.2025.2507744Image stitchingaerial imagesunmanned ariel vehicle (UAV)remote sensingagricultureprecision agriculture |
| spellingShingle | Maria John S. Santhanalakshmi J Amudha Jianfeng Zhou Comparative analysis of image mosaicing techniques for aerial agriculture field imaging European Journal of Remote Sensing Image stitching aerial images unmanned ariel vehicle (UAV) remote sensing agriculture precision agriculture |
| title | Comparative analysis of image mosaicing techniques for aerial agriculture field imaging |
| title_full | Comparative analysis of image mosaicing techniques for aerial agriculture field imaging |
| title_fullStr | Comparative analysis of image mosaicing techniques for aerial agriculture field imaging |
| title_full_unstemmed | Comparative analysis of image mosaicing techniques for aerial agriculture field imaging |
| title_short | Comparative analysis of image mosaicing techniques for aerial agriculture field imaging |
| title_sort | comparative analysis of image mosaicing techniques for aerial agriculture field imaging |
| topic | Image stitching aerial images unmanned ariel vehicle (UAV) remote sensing agriculture precision agriculture |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2025.2507744 |
| work_keys_str_mv | AT mariajohn comparativeanalysisofimagemosaicingtechniquesforaerialagriculturefieldimaging AT ssanthanalakshmi comparativeanalysisofimagemosaicingtechniquesforaerialagriculturefieldimaging AT jamudha comparativeanalysisofimagemosaicingtechniquesforaerialagriculturefieldimaging AT jianfengzhou comparativeanalysisofimagemosaicingtechniquesforaerialagriculturefieldimaging |