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Wang Zhibin

Bio: Wang Zhibin is an academic researcher from Center for Information Technology. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 8, co-authored 29 publications receiving 184 citations.

Papers
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Journal ArticleDOI
TL;DR: Experimental results show that the proposed image segmentation method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions.

66 citations

Journal ArticleDOI
TL;DR: A new extraction and classification algorithm is firstly introduced to recognize leaves from images and a region-labeling algorithm is applied to calculate the insect number and disease areas in the segmented images.
Abstract: Computer vision and image processing technology have been rapidly developed and widely applied in many fields. There are many potential applications in modern agriculture. In this paper, a novel vegetable disease and insect pest recognition method is proposed based on the current computer vision and image processing methods. To investigate the vegetable disease and insect pest state, it is convenient to use images captured using smart phones for judgment. To implement this application, the disease area and the insect number on the leaves should be detected and figured out. So a new extraction and classification algorithm is firstly introduced to recognize leaves from images. Then a region-labeling algorithm is applied to calculate the insect number and disease areas in the segmented images. To deal with the areas of adhesion, a mathematical morphology algorithm is used for separating the objects. The proposed method is implemented on mobile smart devices and tested with field experiments. The experimental...

34 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed an adaptive segmentation method for crop disease images based on K-means clustering, which combines the excess green feature and the a* component of the CIE (L*a*b*) color space.
Abstract: . Disease spot segmentation from crop leaf images is a key prerequisite for disease early warning and diagnosis. To improve the accuracy and stability of disease spot segmentation, an adaptive segmentation method for crop disease images based on K-means clustering is proposed. The approach is based on three stages. First, the excess green feature and the a* component of the CIE (L*a*b*) color space were combined to adaptively learn the initial cluster centers. Second, iterative color clustering of two clusters was conducted using the squared Euclidian distance as the similarity distance. Finally, the distance of a* components between two clusters as the clustering criterion function was used to correct the clustering results. To verify the effectiveness of the proposed method, segmentation experiments were performed on images of three kinds of cucumber diseases and one kind of soybean disease. The results of the experiments were compared with the results obtained using a fixed threshold method, the Otsu method, the traditional K-means clustering method, and the Renyi entropy method, which showed that our adaptive segmentation method was accurate and robust for segmentation of crop disease images.

26 citations

Journal ArticleDOI
TL;DR: The proposed cognitive segmentation method was accurate and robust for segmentation of whitefly images, and will provide a foundation for further identifying these pests.

18 citations

Journal ArticleDOI
TL;DR: In this article, a decision support system for risk assessment of heavy metal pollution in farmland soil is established, in which technologies such as web-based geographic information system, quick response code, radio frequency identification, and web service are introduced as the bases.
Abstract: Heavy metal pollution in farmlands is a serious threat to sustainable agricultural development and has become a major agro-ecological problem that has attracted public concern in China. This study proposes a soil–crop collaborative risk assessment model that aims to assess the potential safety risks of heavy metal pollution in farmland soils by considering the concentrations of heavy metals in soils and the accumulation effects of heavy metals in crops. Based on these effects, a decision support system for risk assessment of heavy metal pollution in farmland soil is established, in which technologies such as web-based geographic information system, quick response code, radio frequency identification, and web service are introduced as the bases. The proposed system is composed of a mobile data acquisition terminal (MDAT) and a web-based information system (WIS). The MDAT, which is a portable computerized device running on the Android platform, is used for data acquisition or query, and the WIS is used for risk assessment, data management, and information visualization. The system is employed in some county-level cities in China for risk assessment and supervision of heavy metal pollution in farmlands. The practical application results show that the system provides highly efficient decision support for risk assessment of heavy metal pollution in farmland soils.

17 citations


Cited by
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Journal ArticleDOI
TL;DR: Sensitivity analyses indicated that metal concentrations and ingestion rate of soil were the predominant contributors to total risk variance, and the adverse health risks induced by exposure to heavy metals contaminated farmland were elevated.

157 citations

Journal ArticleDOI
TL;DR: The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming, to serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture.
Abstract: Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.

129 citations

Journal ArticleDOI
Li Zhenbo, Guo Ruohao1, Li Meng1, Chen Yaru1, Guangyao Li1 
TL;DR: This review extensively reviews 200+ papers of plant phenotyping in the light of its technical evolution, spanning over twenty years, including imaging technologies, plant datasets, and state-of-the-art phenotypesing methods.

119 citations

Journal ArticleDOI
TL;DR: Compared with the traditional K-means algorithm, DBSCAN algorithm, Mean Shift algorithm and ExG-ExR color indices method, the proposed algorithm can successfully segment the tomato leaf images more precisely and efficiently.

90 citations

Journal ArticleDOI
TL;DR: A novel fuzzy set extended form neutrosophic logic based segmentation technique is used to evaluate the region of interest and a new feature set is promising and 98.4% classification accuracy is achieved.

79 citations