Lingxian Zhang

Evaluation of the rural informatization level in four Chinese regions: A methodology based on catastrophe theory

Abstract The paper presents a method that addresses the problem of using catastrophe theory to evaluate the informatization level in four Chinese regions. We developed an index evaluation system that consists of five categories (secondary indices) of Economic strength, Information infrastructure, Information terminal equipment, Human resources and Information utilization, and fourteen tertiary indices as the evaluation index system for the rural informatization level. The effectiveness of this method is tested by evaluating the level of information technology application in four Chinese rural regions (eastern, central, western, and northeastern regions). The results show that the catastrophe progression values (CPV) averaged at 0.806 in mainland China. The CPV for the eastern region is ranked the highest at 0.871, the northeast region second at 0.841, the central region third at 0.553, and the western region the lowest at 0.213. The results are found to be consistent with a priori expectations proving that the catastrophe progression method works well.

Evaluation of learner adoption intention of e‐learning in China: A methodology based on perceived innovative attributes

Abstract E‐learning, as an innovative and alternative way of distance learning, provides a strong challenge to traditional learning with its unique advantage. This paper, on the diffusion of e‐learning adoption in China, investigates peoples perceptions and attitudes toward adopting e‐learning, and explores the factors affecting the e‐learning adoption behaviour from an innovation adoption perspective. Based on the model of Rogers’ innovation adoption theory, every factor of perceived innovative attributes influencing the individual adoption behaviour, namely perceived relative advantage, perceived compatibility, perceived trialability, and perceived observability, will be analysed to test the relationship between the perceived innovative attributes and adoption intention of e‐learning. The result shows that the four perceived innovative attributes do have certain influences on peoples adoption of e‐learning.

A key frame extraction method for processing greenhouse vegetables production monitoring video

A new method for extracting key frames of greenhouse videos was proposed.The method took full considerations of characteristics of greenhouse videos.The method combined the visual saliency and online clustering.The IG algorithm was modified by two parameters confirmed by a new measurement. Research reported in this paper aims to improve the identification of greenhouse vegetable diseases based on the greenhouse monitoring video. It presents a method that combines the visual saliency and online clustering to extract the key frame from greenhouse vegetables monitoring video. Firstly X2 histograms are used to measure the similarity of each frame to the first frame, which eliminates the meaningless frames and improve data processing efficiency and costs. Then, all frames will be converted to HSV color space and a saliency map of each frame is generated based on H component value and S component value. According to the saliency map, the salient region can be obtained. During the process of extracting the salient region, there is a possibility that the information of disease spots is lost. Therefore, morphological method would be utilized to restore the lost information. Finally, online clustering is performed to classify the salient regions into different clusters, and mean pixels value is used to select the key frames. The results indicate that this method can obtain information of entire leaf area of vegetables and extract the key frame effectively.

A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing

A comprehensive color feature and its detection method are proposed.The method can segment disease spots images captured under real field conditions.The method adopts a region growing method based on CCF map to obtain the disease spots segmentation.The method guarantees an accurate input to CNN based disease identification. This paper presents a novel image processing method using color information and region growing for segmenting greenhouse vegetable foliar disease spots images captured under real field conditions. Disease images captured under real field conditions are suffering from uneven illumination and complicated background, which is a big challenge to achieve robust disease spots segmentation. A disease spots segmentation method consisting of two pipelined procedures is proposed in this paper. Firstly a comprehensive color feature and its detection method are presented. The comprehensive color feature (CCF) consists of three color components, Excess Red Index (ExR), H component of HSV color space and b component of Lab color space, which implements powerful discrimination of disease spots and clutter background. Then an interactive region growing method based on the CCF map is used to achieve disease spots segmentation from clutter background. To evaluate the robustness and accuracy, the proposed segmentation method is assessed by cucumber downy mildew images. Results show that the proposed method can achieve accurate and robust segmentation under real field conditions.

A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images

Improving the extraction of cucumber leaf spot disease under complex backgrounds.Redefining the feature distance between pixel xj and clustering center vi.Calculating the two-dimensional neighborhood mean gray value as a sample point.Proposing a new weighting method for gray value and neighborhood gray value. Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM) algorithm is proposed in this paper. First, three runs of the marked-watershed algorithm, based on HSI space, are applied to isolate the target leaf. Second, the distance between the pixel xj and the cluster center vi is defined as xj2-vi2. Third, the pixels neighborhood mean gray value, which constitutes a two-dimensional vector with grayscale information, is calculated as a sample point, rather than FCM grayscale. Finally, the neighborhood mean gray value and pixel gray value are weighted by matrix w. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 129 cucumber disease images in vegetable disease database. Results show that average segmentation error was only 0.12%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications.

Effect of householder characteristics, production, sales and safety awareness on farmers’ choice of vegetable marketing channels in Beijing, China

Purpose Farmers’ selection of vegetable marketing channels directly affects their income and is important to stable vegetable supply and food control. The purpose of this paper is to investigate the farmers’ selection behavior of vegetable marketing channels, and to determine the key factors which affected farmer’ decision making. Design/methodology/approach A total of 191 valid questionnaires were collected from 50 villages in seven main vegetable production districts in Beijing urban areas from September to December 2015, yielding a response rate of 86.8 percent. The multinomial logit model was used for analysis in this study. Findings The results revealed that the farmers mainly selected farmers’ market, cooperative, and wholesaler to sell their vegetables, which comprised 96.57 percent of total vegetable sales. Estimation results showed that cooperative, vegetable acreage, price satisfaction, and slow sales were most important factors which influence positively the probability of opting to sell vegetables at a cooperative rather than at the farmer’s market. For wholesalers, gender of the household head and cooperative had most significantly negative effect, and age had a positive impact on farmer’s choice of market channels. Originality/value The results and implications obtained in the present study could help policymakers to establish a scientific-based and reasonable policy to encourage vegetable producers to participate in the circulation of vegetables in Beijing and guarantee their income in vegetable supply chain. The suggestions of this study could also be used for the improvement of the vegetable sector in other cities facing similar issues.

Analysing levels of China's agricultural domestic support with an optimising model

Abstract The paper analyses the impacts of alteration of agricultural domestic supports on state welfare adopting partial equilibrium theory. In order to find out the appropriate levels of agricultural domestic supports in China, a nonlinear programming approach was utilised, based on constraints of the annual financial budget and the Uruguay Round Agreement on Agriculture, to build an optimisation model for levels of China agricultural domestic support was built to aim at the maximum welfare. The result shows that the maximum levels of China “amber box” support are 50 billion dollars from welfare maximisation in the international agricultural product trade. The amber box support is about 174 billion RMB yuan according to the de minimis levels of China with 8.5%. The national annual budgetary expenditure for agriculture was 175.445 billion RMB yuan approximately in 2003, the amber box support of which was only 55.143 billion RMB yuan.

A Portable Measurement Instrument for the Measurement of Water Body Chlorophyll-a in the Support of Fluorescence Detection

Chlorophyll-a was regarded as the important indicator to describe the marine primary production because the chlorophyll a content of phytoplankton in the ocean is related to its photosynthesis production. The concentration of chlorophyll a is also the major parameter to evaluate marine water quality, organic pollution and detect the fishing ground, and the temporal and spatial variation of chlorophyll a contains the basic information of sea areas. Based on the spectral characteristics of chlorophyll fluorescence, this document recommends a new dual optical detecting instrument for the measurement of chlorophyll-a concentration, the microcontroller MSP430F149 as the key control module, by controlling the ultra-high brightness LED which wavelength is 450nm to excite chlorophyll a to produce the fluorescent signal about 680nm , at the same time this LED is used as reference light, the dual-optical structure exclude the light fluctuations due to the impact of test results. At last, we get the relationship between relative fluorescence intensity and chlorophyll-a concentration with the spectrophotometer, we find the system has the good linear consistency when measures the low concentrations of chlorophyll-a.

Evaluation of the Rural Informatization Level in Central China Based on Catastrophe Progression Method

This paper developed a methodology based on the catastrophe theory for estimating the rural informatization level in central China, and took evaluation of the rural informatization level among the six provinces of central China as an example to test the effectiveness of the method. Taking data from reference and constructing the hierarchy based on catastrophe progression method, it was calculated the scores of rural informatization level among the six provinces of central China using normalizing formula. The results are found to be coincident with practical situation, so it proves the catastrophe progression method works well.

A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network

Abstract Manual approaches to recognize cucumber diseases are often time-consuming, laborious and subjective. A deep convolutional neural network (DCNN) was proposed to conduct symptom-wise recognition of four cucumber diseases, i.e., anthracnose, downy mildew, powdery mildew, and target leaf spots. The symptom images were segmented from cucumber leaf images captured under field conditions. In order to decrease the chance of overfitting, data augmentation methods were utilized to enlarge the datasets formed by the segmented symptom images. With the augmented datasets containing 14,208 symptom images, the DCNN achieved good recognition results, with an accuracy of 93.4%. In order to compare the results of the DCNN, comparative experiments were conducted using conventional classifiers (Random Forest and Support Vector Machines), as well as AlexNet. Results showed that the DCNN was a robust tool for recognizing the cucumber diseases in field conditions.