Optimizing the Spatial Layout of Agricultural Irrigation Sprinklers Using Remote Sensing Data: An Adaptive Incremental <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Means Clustering Algorithm
The layout of sprinklers is crucial in agricultural irrigation systems, and agricultural remote sensing technology plays a key role in extracting plant distribution data for designing efficient sprinkler layouts. However, traditional manual methods struggle to handle the complexity and scale of plan...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11015806/ |
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| author | Jing Geng Shangxian Zhao Yifei Wang Qi Li |
| author_facet | Jing Geng Shangxian Zhao Yifei Wang Qi Li |
| author_sort | Jing Geng |
| collection | DOAJ |
| description | The layout of sprinklers is crucial in agricultural irrigation systems, and agricultural remote sensing technology plays a key role in extracting plant distribution data for designing efficient sprinkler layouts. However, traditional manual methods struggle to handle the complexity and scale of plant distribution data. To address this, we propose an adaptive incremental <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-means (AIK-means) clustering algorithm for sprinkler layout optimization. AIK-means partitions plant objects into clusters, determining a centroid for each cluster. By placing sprinklers at these centroids, the algorithm ensures high irrigation coverage and minimizes water waste. AIK-means iteratively updates the centroids, assigning plant objects to clusters based on distance constraints to guarantee full coverage within each cluster. New centroids are introduced for plant objects not yet irrigated, and the centroids are updated within these new clusters to ensure validity. In addition, AIK-means integrates an adaptive adjustment mechanism to prevent excessive clustering of centroids, thereby minimizing overlapping sprinkler coverage. Experimental results on real plant distribution datasets extracted from agricultural remote sensing images demonstrate that AIK-means outperforms widely-used clustering algorithms, achieving a significant improvement of at least 90% in the coverage-to-overlap ratio metric. |
| format | Article |
| id | doaj-art-d8904c5e37a945f48156992b7bf4dab0 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-d8904c5e37a945f48156992b7bf4dab02025-08-20T03:32:42ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118149741498710.1109/JSTARS.2025.357384011015806Optimizing the Spatial Layout of Agricultural Irrigation Sprinklers Using Remote Sensing Data: An Adaptive Incremental <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Means Clustering AlgorithmJing Geng0https://orcid.org/0000-0003-4076-6134Shangxian Zhao1https://orcid.org/0009-0004-8633-944XYifei Wang2https://orcid.org/0009-0006-1439-1681Qi Li3https://orcid.org/0000-0003-1896-7044School of Computer Science, Beijing Institute of Technology, Beijing, ChinaSchool of Computer Science, Beijing Institute of Technology, Beijing, ChinaSchool of Computer Science, Beijing Institute of Technology, Beijing, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing, ChinaThe layout of sprinklers is crucial in agricultural irrigation systems, and agricultural remote sensing technology plays a key role in extracting plant distribution data for designing efficient sprinkler layouts. However, traditional manual methods struggle to handle the complexity and scale of plant distribution data. To address this, we propose an adaptive incremental <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-means (AIK-means) clustering algorithm for sprinkler layout optimization. AIK-means partitions plant objects into clusters, determining a centroid for each cluster. By placing sprinklers at these centroids, the algorithm ensures high irrigation coverage and minimizes water waste. AIK-means iteratively updates the centroids, assigning plant objects to clusters based on distance constraints to guarantee full coverage within each cluster. New centroids are introduced for plant objects not yet irrigated, and the centroids are updated within these new clusters to ensure validity. In addition, AIK-means integrates an adaptive adjustment mechanism to prevent excessive clustering of centroids, thereby minimizing overlapping sprinkler coverage. Experimental results on real plant distribution datasets extracted from agricultural remote sensing images demonstrate that AIK-means outperforms widely-used clustering algorithms, achieving a significant improvement of at least 90% in the coverage-to-overlap ratio metric.https://ieeexplore.ieee.org/document/11015806/Agricultural remote sensingclusteringirrigation systemssprinkler layout |
| spellingShingle | Jing Geng Shangxian Zhao Yifei Wang Qi Li Optimizing the Spatial Layout of Agricultural Irrigation Sprinklers Using Remote Sensing Data: An Adaptive Incremental <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Means Clustering Algorithm IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Agricultural remote sensing clustering irrigation systems sprinkler layout |
| title | Optimizing the Spatial Layout of Agricultural Irrigation Sprinklers Using Remote Sensing Data: An Adaptive Incremental <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Means Clustering Algorithm |
| title_full | Optimizing the Spatial Layout of Agricultural Irrigation Sprinklers Using Remote Sensing Data: An Adaptive Incremental <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Means Clustering Algorithm |
| title_fullStr | Optimizing the Spatial Layout of Agricultural Irrigation Sprinklers Using Remote Sensing Data: An Adaptive Incremental <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Means Clustering Algorithm |
| title_full_unstemmed | Optimizing the Spatial Layout of Agricultural Irrigation Sprinklers Using Remote Sensing Data: An Adaptive Incremental <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Means Clustering Algorithm |
| title_short | Optimizing the Spatial Layout of Agricultural Irrigation Sprinklers Using Remote Sensing Data: An Adaptive Incremental <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-Means Clustering Algorithm |
| title_sort | optimizing the spatial layout of agricultural irrigation sprinklers using remote sensing data an adaptive incremental inline formula tex math notation latex k tex math inline formula means clustering algorithm |
| topic | Agricultural remote sensing clustering irrigation systems sprinkler layout |
| url | https://ieeexplore.ieee.org/document/11015806/ |
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