Segmentation of Crop Disease Images with an Improved K-means Clustering Algorithm

Wang, Zhibin; Wang, Kaiyi; Pan, Shouhui; Han, Yanyun

doi:10.13031/aea.12205

Cited by 33 publications

(16 citation statements)

References 30 publications

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“…Wang pointed out that disease spot segmentation from crop leaf images is a key prerequisite for disease early warning and diagnosis. In order to improve the accuracy and stability of disease spot segmentation, he proposed an adaptive segmentation method of crop disease images based on K-means clustering [5]. Khan proposed an improved K-means clustering algorithm for intelligent image segmentation, which used an adaptive histogram-based initial parameter estimation process [6].…”

Section: Related Workmentioning

confidence: 99%

Influence of Management Education on Enterprise Scientific and Technological Innovation Based on K-Means Clustering Algorithm

Liu

et al. 2022

Mathematical Problems in Engineering

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Scientific and technological innovation is the source of the survival and development of enterprises and the key to the realization of the goal of prosperity. In recent years, more and more companies have begun to focus on technological innovation, but the results are not significant, so companies have begun to explore the factors that affect their technological innovation. The management is the helm of the development of the enterprise, the main body of the company’s actual production activities, and the direct person in charge of the company’s management. Its influence on the innovation of the enterprise is self-evident, and the education level of the management directly determines the manager’s ability and vision. However, the current research on management mainly focuses on the position change of management and the rights of management and does not involve the level of education of management. Based on this, this article started from the management education and subdivided it with the K-means clustering algorithm, so as to explore the impact of management education on the technological innovation of enterprises. The experiment showed that there was a significant positive correlation between the educational level of management and the technological innovation ability of enterprises, and the correlation coefficient was 1.521. It fully shows that the management with a higher education background will promote the enterprise to carry out scientific and technological innovation practice and continuously improve the enterprise’s innovation ability.

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Section: Related Workmentioning

confidence: 99%

Influence of Management Education on Enterprise Scientific and Technological Innovation Based on K-Means Clustering Algorithm

Liu

et al. 2022

Mathematical Problems in Engineering

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“…Based on the previous literature, machine learning techniques such as k-means and sparse generative topographic mapping (SGTM) are used to conduct clustering and forecasting in this study. K-means, which can divide data into k predetermined classes based on minimizing an error function, has been applied by many researchers in a wide range of domains because of its simplicity, efficiency and ease of convergence [70,71]. To achieve simultaneous data visualization and clustering, SGTM is developed by modifying the conventional GTM algorithm, which is always used in clustering motor unit action potentials and offers an approach of visualization as an effective tool for machine learning.…”

Section: Text Miningmentioning

confidence: 99%

Development of Morphology Analysis-Based Technology Roadmap Considering Layer Expansion Paths: Application of TRIZ and Text Mining

2020

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Morphology analysis (MA)-based roadmapping has been considered an effective means to support the process of technology innovation in a business environment. However, previous research on MA-based roadmaps has commonly focused on the process of developing existing technology roadmaps (TRMs), while the paths of layer expansion for seeking new opportunities is rarely a focus. Thus, the aim of this research is to develop MA-based TRMs by utilizing MA to describe the characteristics of the technology and product layers in the TRMs and apply the improved theory of inventive problem solving (TRIZ) inventive principles to establish innovation paths for new opportunities with the aid of text mining tools. This study suggests using a morphological matrix to construct existing TRMs by calculating the correlations among different technology and product nodes and two sparse generative topographic mapping (SGTM)-based maps to discover new technology and product opportunities by identifying technology and product development trends and innovation elements in sparse areas, which is the objective of simplifying TRIZ application. To illustrate the performance of the proposed approach, a case study is conducted using patents and product manuals for underwater vehicles, which are becoming popular high-tech and secure tools to explore sub-sea resources. This approach contributes by suggesting a semi-autonomous and systematic procedure to extend the existing MA-based TRM and simplifying TRIZ application according to the occurrence frequency of the keywords.

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“…From Equations (4) and (6), d p (s) = 1 − 1 s can be obtained as the diffusion ratio of the level set evolution.…”

Section: Distance Regularization Termmentioning

confidence: 99%

“…In particular, those algorithms based on K-means clustering, fuzzy c-means clustering (FCM) and spectral clustering are the most widely used in image segmentation problems [5]. Wang et al [6] presented an adaptive segmentation method for crop disease images based on K-means clustering to improve the accuracy and stability of disease spot segmentation. As the FCM algorithm is sensitive to noise and selection to the initial cluster centers, some improvements to the FCM model have been investigated for both the estimation of the intensity inhomogeneity and segmentation of magnetic resonance image data [7].…”

Section: Introductionmentioning

confidence: 99%

An Image Segmentation Method Based on Improved Regularized Level Set Model

Sun

Meng

et al. 2018

Applied Sciences

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When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well.

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Segmentation of Crop Disease Images with an Improved K-means Clustering Algorithm

Cited by 33 publications

References 30 publications

Influence of Management Education on Enterprise Scientific and Technological Innovation Based on K-Means Clustering Algorithm

Influence of Management Education on Enterprise Scientific and Technological Innovation Based on K-Means Clustering Algorithm

Development of Morphology Analysis-Based Technology Roadmap Considering Layer Expansion Paths: Application of TRIZ and Text Mining

An Image Segmentation Method Based on Improved Regularized Level Set Model

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