Using improved density peak clustering algorithm for flower cluster identification and apple central and peripheral flower detection
•A method for apple flowers recognition based on YOLOv8n model was proposed.•An improved Single-Layer DPC algorithm was used to automatically determine different clusters.•A Double-Layer DPC algorithm was applied to rectify any offsets of centres of clusters.•The proposed method provided reference f...
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| Vydáno v: | Computers and electronics in agriculture Ročník 232; s. 110095 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.05.2025
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| ISSN: | 0168-1699 |
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| Abstract | •A method for apple flowers recognition based on YOLOv8n model was proposed.•An improved Single-Layer DPC algorithm was used to automatically determine different clusters.•A Double-Layer DPC algorithm was applied to rectify any offsets of centres of clusters.•The proposed method provided reference for mechanical and chemical thinning of flowers.
Apple flower detection and positioning are crucial for the mechanical and chemical thinning of flowers, where typically only one or two of the strongest flowers in each cluster are retained. An improved method is proposed that leverages the YOLOv8n model for accurate flower detection. The DPC algorithm is enhanced to automatically determine the number of flower clusters and accurately identify the central flowers within those clusters. To evaluate the performance of the enhanced Single-Layer DPC algorithm, it was compared with several other clustering methods, including DPC, DPC with Shared Nearest Neighbors (DPC-SNN), K-means, K-medoids, Gaussian Mixture Model (GMM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Spectral Clustering (SC), minibatch and 3W-PEDP. The results demonstrated that the proposed method achieved that the Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI) were 0.7037 and 0.6043, respectively, on the Flame dataset, surpassing the highest scores obtained by other methods (0.5886 and 0.5116, respectively). Additionally, the improved algorithm reduced the deviation between the clustering center produced by the Single-Layer DPC and the true central flower. Overall, the algorithm effectively reduces clustering center deviations, showcasing its capability to accurately detect and position apple flowers. |
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| AbstractList | Apple flower detection and positioning are crucial for the mechanical and chemical thinning of flowers, where typically only one or two of the strongest flowers in each cluster are retained. An improved method is proposed that leverages the YOLOv8n model for accurate flower detection. The DPC algorithm is enhanced to automatically determine the number of flower clusters and accurately identify the central flowers within those clusters. To evaluate the performance of the enhanced Single-Layer DPC algorithm, it was compared with several other clustering methods, including DPC, DPC with Shared Nearest Neighbors (DPC-SNN), K-means, K-medoids, Gaussian Mixture Model (GMM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Spectral Clustering (SC), minibatch and 3W-PEDP. The results demonstrated that the proposed method achieved that the Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI) were 0.7037 and 0.6043, respectively, on the Flame dataset, surpassing the highest scores obtained by other methods (0.5886 and 0.5116, respectively). Additionally, the improved algorithm reduced the deviation between the clustering center produced by the Single-Layer DPC and the true central flower. Overall, the algorithm effectively reduces clustering center deviations, showcasing its capability to accurately detect and position apple flowers. •A method for apple flowers recognition based on YOLOv8n model was proposed.•An improved Single-Layer DPC algorithm was used to automatically determine different clusters.•A Double-Layer DPC algorithm was applied to rectify any offsets of centres of clusters.•The proposed method provided reference for mechanical and chemical thinning of flowers. Apple flower detection and positioning are crucial for the mechanical and chemical thinning of flowers, where typically only one or two of the strongest flowers in each cluster are retained. An improved method is proposed that leverages the YOLOv8n model for accurate flower detection. The DPC algorithm is enhanced to automatically determine the number of flower clusters and accurately identify the central flowers within those clusters. To evaluate the performance of the enhanced Single-Layer DPC algorithm, it was compared with several other clustering methods, including DPC, DPC with Shared Nearest Neighbors (DPC-SNN), K-means, K-medoids, Gaussian Mixture Model (GMM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Spectral Clustering (SC), minibatch and 3W-PEDP. The results demonstrated that the proposed method achieved that the Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI) were 0.7037 and 0.6043, respectively, on the Flame dataset, surpassing the highest scores obtained by other methods (0.5886 and 0.5116, respectively). Additionally, the improved algorithm reduced the deviation between the clustering center produced by the Single-Layer DPC and the true central flower. Overall, the algorithm effectively reduces clustering center deviations, showcasing its capability to accurately detect and position apple flowers. |
| ArticleNumber | 110095 |
| Author | Xiang, Shiyu Wang, Jiachen Wang, Lei Song, Huaibo Geng, Mingyang Shang, Yuying |
| Author_xml | – sequence: 1 givenname: Mingyang surname: Geng fullname: Geng, Mingyang organization: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China – sequence: 2 givenname: Yuying surname: Shang fullname: Shang, Yuying organization: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China – sequence: 3 givenname: Shiyu surname: Xiang fullname: Xiang, Shiyu organization: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China – sequence: 4 givenname: Jiachen surname: Wang fullname: Wang, Jiachen organization: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China – sequence: 5 givenname: Lei surname: Wang fullname: Wang, Lei organization: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China – sequence: 6 givenname: Huaibo surname: Song fullname: Song, Huaibo email: songhuaibo@nwsuaf.edu.cn organization: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China |
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| SubjectTerms | agriculture algorithms Apple Flower apples data collection Deep Learning Density Peak Clustering Algorithm electronics Flower Cluster Identification flowers |
| Title | Using improved density peak clustering algorithm for flower cluster identification and apple central and peripheral flower detection |
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