Cost-effective and adaptive clustering algorithm for stream processing on cloud system

Xia, Yue; Fang, Junhua; Chao, Pingfu; Pan, Zhicheng; Shang, Jedi S.

doi:10.1007/s10707-021-00442-1

Cited by 3 publications

(3 citation statements)

References 45 publications

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“…The WDVI construction method was used and in this study five weed-sensitive indices were selected (Wan et al, 2020;Xia et al, 2021). Five traditional vegetation indices such as GNDVI (Green Normalized Difference Vegetation Index), NDVI (Normalized Difference Vegetation Index), LCI(Leaf Chlorophyll Index), NDRE(Normalized Differential Red Edge…”

Section: Results Of Vegetation Index For Weed Identification In Ricementioning

confidence: 99%

“…The WDVI construction method was used and in this study five weed-sensitive indices were selected ( Wan et al., 2020 ; Xia et al., 2021 ). Five traditional vegetation indices such as GNDVI(Green Normalized Difference Vegetation Index), NDVI(Normalized Difference Vegetation Index), LCI(Leaf Chlorophyll Index), NDRE(Normalized Differential Red Edge vegetation inde), and OSAVI(Optimized Soil Adjusted Vegetation Index) were selected for comparison, and a total of ten vegetation indices were used to generate pseudo-color maps for the identification of the rice weed vegetation index, and the results are shown in Figure 3 .…”

Section: Results and Analysismentioning

confidence: 99%

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Research on weed identification method in rice fields based on UAV remote sensing

Jin²,

Guo³

et al. 2022

Front. Plant Sci.

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Rice is the world’s most important food crop and is of great importance to ensure world food security. In the rice cultivation process, weeds are a key factor that affects rice production. Weeds in the field compete with rice for sunlight, water, nutrients, and other resources, thus affecting the quality and yield of rice. The chemical treatment of weeds in rice fields using herbicides suffers from the problem of sloppy herbicide application methods. In most cases, farmers do not consider the distribution of weeds in paddy fields, but use uniform doses for uniform spraying of the whole field. Excessive use of herbicides not only pollutes the environment and causes soil and water pollution, but also leaves residues of herbicides on the crop, affecting the quality of rice. In this study, we created a weed identification index based on UAV multispectral images and constructed the WDVINIR vegetation index from the reflectance of three bands, RE, G, and NIR. WDVINIR was compared with five traditional vegetation indices, NDVI, LCI, NDRE, and OSAVI, and the results showed that WDVINIR was the most effective for weed identification and could clearly distinguish weeds from rice, water cotton, and soil. The weed identification method based on WDVINIR was constructed, and the weed index identification results were subjected to small patch removal and clustering processing operations to produce weed identification vector results. The results of the weed identification vector were verified using the confusion matrix accuracy verification method and the results showed that the weed identification accuracy could reach 93.47%, and the Kappa coefficient was 0.859. This study provides a new method for weed identification in rice fields.

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Section: Results Of Vegetation Index For Weed Identification In Ricementioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Research on weed identification method in rice fields based on UAV remote sensing

Jin²,

Guo³

et al. 2022

Front. Plant Sci.

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“…CeAC is another recently proposed completely online clustering technique based on grid density [25]. It is a cost-effective and adaptive clustering technique that can increase computational efficiency while maintaining the clustering result's accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

et al. 2022

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As applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering’s primary objective and goal are to acquire insights into incoming data. Recognizing all possible patterns in data streams that enter at variable rates and structures and evolve over time is critical for acquiring insights. Analyzing the data stream has been one of the vital research areas due to the inevitable evolving aspect of the data stream and its vast application domains. Existing algorithms for handling data stream clustering consider adding various data summarization structures starting from grid projection and ending with buffers of Core-Micro and Macro clusters. However, it is found that the static assumption of the data summarization impacts the quality of clustering. To fill this gap, an online clustering algorithm for handling evolving data streams using a tempo-spatial hyper cube called BOCEDS TSHC has been developed in this research. The role of the tempo-spatial hyper cube (TSHC) is to add more dimensions to the data summarization for more degree of freedom. TSHC when added to Buffer-based Online Clustering for Evolving Data Stream (BOCEDS) results in a superior evolving data stream clustering algorithm. Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering metrics.

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A Brief Study of Adaptive Clustering for Self-aware Machine Analytics

Baalamurugan,

Phutela

2024

Disruptive Technologies and Digital Transformations for Society 5.0

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Cost-effective and adaptive clustering algorithm for stream processing on cloud system

Cited by 3 publications

References 45 publications

Research on weed identification method in rice fields based on UAV remote sensing

Research on weed identification method in rice fields based on UAV remote sensing

A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

A Brief Study of Adaptive Clustering for Self-aware Machine Analytics

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