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There is rich knowledge in expert systems that can be used to solve practical problems, but its promotion and application must rely on information facilities. The application of both computers and the Internet for Chinese farmers are not common, which leads to restrictions on the promotion and application of expert systems in rural areas of China. On the other hand, the existing call centers lack a professional knowledge base and the method of automatically calling the knowledge base in real-time, which makes it difficult to meet the needs of users wanting to obtain knowledge in a timely manner. To address these problems, a call center embedded in an expert system inference algorithm and knowledge base for farmers to obtain agricultural knowledge through mobile phones or fixed-line telephones was established. By studying the event-condition-action-based (ECA-based) database triggering model, remote method invocation-based (RMI-based) communication and iterative dichotomiser 3 algorithm-based (ID3-based) parameter extraction, the cohesion between the call center and the expert system was realized. The agricultural knowledge audio acquisition model was then coupled with the call center and the expert system was constructed, allowing farmers to acquire agricultural knowledge through mobile phones or fixed phones with fast responses. When used for cotton disease diagnosis, it can achieve a high diagnostic success rate (above 75%) when at least three disease symptoms are input into the expert system via the voice call, which provides an effective channel for Chinese farmers to obtain agricultural knowledge. It presents good application prospects in China, where 5G technology is currently developing rapidly.

In this study, a cotton disease diagnosis method that uses a combined algorithm of case-based reasoning (CBR) and fuzzy logic was designed and implemented. It focuses on the prevention, diagnosis and control of diseases affecting cotton production in China. Conventional methods of disease diagnosis are primarily based on CBR with reference to user-provided symptoms; however, in most cases, user-provided symptoms do not fully meet the requirements of CBR. To address this problem, fuzzy logic is incorporated into CBR to allow for more flexible and accurate models. With the help of CBR and fuzzy reasoning, three diagnostic results can be obtained by the cotton disease diagnosis system (CDDS) constructed in this study: success, success but not exact and failure. To verify the reliability of the CDDS and its ability to diagnose cotton diseases, its diagnostic accuracy and stability were analyzed and compared with the results obtained by the traditional expert scoring method. The analysis results reveal that the CDDS can achieve a high diagnostic success rate (above 90%) and better diagnostic stability than the traditional expert scoring method when at least four disease symptoms are input. The CDDS provides an independent and objective source of information to assist farmers in the diagnosis and prevention of cotton diseases.

Leafy vegetables are consumed in most daily xdiets worldwide. As living standards improve, food quality, safety requirements, and nutrition are becoming increasingly important to consumers when purchasing leafy vegetables. This study proposes an evaluation and traceability method that can be used to track the nutritional quality of leafy vegetables. Employing the principles of the Hazard Analysis and Critical Control Point (HACCP) system combined with fault tree analysis (FTA), a traceability model for the entire production and sale process of leafy vegetables is constructed. Four common leafy vegetables, spinach, rape, lettuce, and celery are examined in this research to establish a nutritional quality index system using fuzzy mathematics subordinate function method to evaluate nutritional quality. A nutritional quality and safety traceability system based on browser/server architecture and quick response (QR) code is then designed and developed for full traceability of leafy vegetable quality. This method can ensure food safety and hygiene through the control of key factors affecting food safety throughout the entire supply chain process.

There is a global shortage of fresh water to meet the clean production of field crops. Water-saving irrigation can increase the production of field crops, but under certain conditions it can be overly water-saving and unsustainable, particularly in complex terrains in China where there is already a shortage of water resources. In light of the insufficient water resources and partial inequality in China, several investigators in China and abroad have researched irrigation nozzles and pumps, but there is little information on the impact of the environment on crop irrigation. The purpose of this study was to provide a fast and effective crop water demand prediction model to estimate the water requirement during the growth period of crops, to achieve the purpose of applying water according to the demand of the crops, and to optimize the irrigation network to determine the optimal network structure in order to reduce irrigation energy consumption in the process. Specifically, this study analyzed the environmental factors, namely, the soil moisture, soil electrical conductivity, air temperature, and light intensity, affecting the physiological water demand of field crops. Relevant sensors were used in the field, and environmental information was collected. This study used the collected environmental data to build a water demand prediction model based on the back propagation (BP) neural network. It also carried out optimized layout and hydrodynamic analyses of the drip irrigation network and identified the best pipe network arrangement. On the basis of the BP neural network, the remarkable R amount of 0.98963 was obtained for the water demand prediction model. The mean square error (MSE) value was 0.00857724. We also found that the drip irrigation pipe network in the H-shaped arrangement was more suitable for field crop irrigation than the comb-shaped and fish bone-shaped arrangements. This study employed the common field crops in China as the research object, used the wireless sensing technology to obtain the field environment information quickly, and constructed a water demand prediction model with high prediction accuracy. Finally, this study realized the precise control of field crop irrigation, reduced the waste of agricultural water, and achieved sustainable field crop production.

The purpose of this article is to examine the impact of rural population aging on farmers’ cleaner production behavior through the intervening effects of three aspects (learning capacity, factor substitution and behavior imitation). The data used in this article were collected from a questionnaire survey of 916 farmers in the main grain producing provinces of the North China Plain. First, confirmatory factor analysis was carried out, and then data were analyzed through structural equation modeling using the bootstrap methodology in AMOS 24. The findings indicate that rural population aging inhibits farmers’ cleaner production behavior through learning capacity. In contrast, rural population aging promotes farmers’ cleaner production behavior through factor substitution and behavior imitation. From the perspective of both challenges and opportunities, this article investigates the impact mechanism and comprehensive effect of rural population aging on farmers’ cleaner production behavior, so as to explore new ways to promote cleaner agricultural production under the background of an accelerated aging the of rural labor force and provide reference for the formulation of relevant policies.

Against the background of the depletion of fossil fuel energy sources and environmental degradation, solar greenhouses represent an efficient and sustainable agricultural production method in China. The precise measurement and control of the position of the rolling shutter and rolling film is not only one of the core issues in the modification of the greenhouse environment in actual production but also the basis for improving the cleaner production efficiency of solar greenhouses. In this study, a mathematical algorithm for the control of a rolling shutter and rolling film was developed and is based on the simplification of the circular arc of the greenhouse surface. The “Liaoshen-IV” solar greenhouse was used as an example to illustrate the calculation steps of the algorithm. Any position on the light surface can be determined by calculating the angle at the bottom of the back wall of the greenhouse so that the position of the rolling shutter and the rolling film can be measured accurately by the angle sensor. Experiments using a scale model with one or two surface segments were conducted to demonstrate the advantages of the control algorithm with regard to the accuracy and stability compared with the existing control method of a time-delay relay. The relative errors of the control algorithm were stable and less than 1%. The use of the algorithm improved the control accuracy of the rolling shutter and rolling film and the average relative error was reduced by 6.37% in the first stage and by 3.40% in the second stage compared with the time-delay relay. The precise measurement and control of the position of the rolling shutter and rolling film in a solar greenhouse demonstrated in this study has great significance for practical applications. The proposed method may result in a reduction in energy consumption and greenhouse gas (GHG) emissions in greenhouse environments, thereby contributing to the improvement of resource efficiency and a cleaner production. The study also provides application examples and theoretical references for the automation of rolling shutters and rolling film in greenhouses worldwide.

The purpose aims to examine the key factors influencing Chinese consumer’s purchasing behaviour of eco‐friendly food in China giving its context as an emerging economy and its rapidly rising importance in the world eco‐friendly food market. This paper adopts and extends the Responsible Environmental Behaviour (REB) theory by empirically testing key psychosocial factors influencing the purchase intention of eco‐friendly food and the moderating effects of consumers’ demographic characteristics on the relationship between the key psychosocial factors and the purchase intention. A number of hypotheses are proposed. A questionnaire was designed and distributed via online survey in Beijing, China. A total of 239 valid responses were received. The empirical data were used to test the research hypotheses using the hierarchical multiple regression analysis. The research finds that the personality factors in the REB model (i.e., pro‐environmental attitudes, the internal locus of control and personal responsibly) have significant positive effects on the consumers’ eco‐friendly food purchase intention. Such effect is stable across consumers with different income levels. On the other hand, the knowledge–skill factors in the REB model do not have significant effect on the purchase intention of consumers. This study contributes to a better understanding of factors affecting eco‐friendly food consumption intention in China and the behavioural characteristics of consumers in developing countries. Moreover, the findings also shed light on the applicability of the REB theory in emerging economies and a specific industrial context.

Toxic and hazardous sulfur dioxide (SO2) residue reduction is not only one of the core issues in the preservation process with SO2 solid antistaling agents in actual table grapes cold chain, but also one of the important ways to improve the cleaner production efficiency of the table grapes. The aim of this paper is to propose an improved preservation process for the table grapes in cold chain by combining the fumigation treatment with the slow-release SO2 solid antistaling agent. The process was performed to reduce the toxic and hazardous SO2 residue of the table grapes, improve the cleaner production efficiency of the cold chain, guarantee the quality and safety of the table grapes, and finally enhance the human health. The slow-release SO2 solid antistaling agent was developed with no pricking holes and relatively stable release rate of the SO2. According to the comprehensive analysis of the fumigation treatment condition determination, the slow-release SO2 solid antistaling agent performance, the table grapes quality performance, the SO2 residue performance and the evaluation on improved preservation process, the proposed improved preservation process with SO2 residue reduction could effectively reduce the bleaching injury, the decay rate, the threshing rate and the toxic and hazardous SO2 residue of the table grapes. There was no bleaching injury after the fumigation treatment at the optimal fumigation treatment conditions with 5000 ppm fumigation concentration and 20 min fumigation time. The maximization reduction range of the average decay rate and the average threshing rate of the table grapes was about 91.3% and 44.8%, while the maximization reduction range of the average SO2 residue in table grapes peels was about 44.7%. The proposed improved preservation process could be extended for other cold chain applications to reduce their SO2 residue and improve their cleaner production efficiency.

China is the largest producer and consumer of vegetables, its vegetable industry is playing an important role in the domestic agricultural sector and global vegetable export market. It is important to promote the long-term sustainable development of Chinese vegetable industry for food security and quality of people’s lives. To find out the intrinsic way to promote the development of Chinese vegetable industry, this paper analyzed the influencing factors of Chinese vegetable production by utilizing the LMDI method and demonstrated the spatial-temporal characteristics of vegetable production through application of the ArcGIS spatial autocorrelation analysis method. The results showed that the influencing factors of vegetable production were the cultivated land area, multiple cropping index, vegetable planting proportion and vegetable yield per hectare in China. The major driving factor had changed from vegetable planting proportion to vegetable yield per hectare. The influencing degrees of factors on vegetable production are different in different regions, regionalization is therefore a major feature of Chinese vegetable production. The government should take production technology, regionalization-driven effect, and marketing integration into consideration to promote the development of Chinese vegetable industry. Keywords: Vegetable production, Influencing factors, Contribution ratio, Logarithmic mean Divisia index (LMDI)

Safe food is healthy, nutritious and environmentally friendly by green, sustainable and clean production. The objectives of this study were to examine factors influencing the consumers' purchase intention and willingness to pay a premium price for safe vegetables. To achieve the objectives, this paper developed the model of consumers' purchase safe vegetables. Based on 840 valid questionnaires from Beijing of China, the model was confirmed by the logistic regression method. The results showed that food safety, strict production and processing, and healthy environment were the top three perceived advantages of safe vegetables for consumers. In addition, 93.0% and 87.1% of consumers were familiar with organic vegetables and green vegetables, respectively. Two-thirds of consumers identified safe vegetables via certification labels on vegetable packages. Furthermore, 67.6% of consumers were willing to buy safe vegetables and 65.8% would pay a premium price for safe vegetables. Meanwhile, consumers' purchase intentions for safe vegetables were positively affected by family food expenditure, children, familiarity, differential cognition, safety awareness, nutritional health, packaging, label trust, and online shopping experience but price level, safety status, and freshness had a negative effect. Consumers’ willingness to pay a premium price for safe vegetables was positively affected by family income, familiarity, differential cognition, safety awareness, nutritional health, packaging, label trust, and online shopping experience but family food expenditure, price level, price fluctuations, and safety status had a negative effect. The findings of this study have important implications to improve the consumption and guide cleaner production of safe vegetables. The findings obtained can also provide references for other similar studies in other areas.

Growth-related traits, such as aboveground biomass and leaf area, are critical indicators to characterize the growth of greenhouse lettuce. Currently, nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. In this study, a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network (CNN). Taking lettuce images as the input, a CNN model was trained to learn the relationship between images and the corresponding growth-related traits, i.e., leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA). To compare the results of the CNN model, widely adopted methods were also used. The results showed that the values estimated by CNN had good agreement with the actual measurements, with R2 values of 0.8938, 0.8910, and 0.9156 and normalized root mean square error (NRMSE) values of 26.00, 22.07, and 19.94%, outperforming the compared methods for all three growth-related traits. The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno. Generalization tests were conducted by using images of Tiberius from another growing season. The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits, with R2 values of 0.9277, 0.9126, and 0.9251 and NRMSE values of 22.96, 37.29, and 27.60%. The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce.

Village rules and formal environmental regulations are of great significance for standardizing farmers’ cleaner production behavior, promoting green transformation of agriculture and realizing sustainable development of agriculture. Based on the survey data of 946 farmers in five provinces of China, taking seed coating technology, soil testing and formulated fertilization technology, subsoiling tillage technology, green technology for pest and disease control and straw returning technology as examples, this article empirically analyzes the impact of village rules and formal environmental regulations on farmers’ cleaner production behavior by using the multivariate probit model. When formal environmental regulations are relatively lacking or weak, village rules can be used as a useful supplement to formal environmental regulations to promote farmers’ participation in cleaner production. Based on this, this article argues that the important reason for formal environmental regulations falling into relative system failure is that village rules have not been paid enough attention in promoting farmers’ cleaner production behavior. In the future, we should not only continue to strengthen the role of formal environmental regulations in farmers’ cleaner production, but also cultivate the informal institution represented by the village rules, and build the regulatory system of mutual support between informal institution and formal institution.

Freshwater aquaculture is the main form of aquaculture in inland areas, and pollution is a significant challenge. The aim of this work was to propose an effective risk assessment method for aquaculture to predict pollutant bioaccumulation in fish tissue based on water environment variables. This method was designed to assess the risk of pollutant levels in fish tissue that exceed China’s food safety standards. Firstly, field observation and investigation were conducted to analyze the migration mechanisms of the main pollutants between fish tissue and the water environment. Secondly, a classification and regression tree (CART) and generalized additive model (GAM) nexus method was used to analyze the environmental variables related to pollutants in the water environment and establish prediction models. Finally, models of the bio-reaction kinetics were established to predict the pollutant bioaccumulation in fish tissue. The experimental results indicate that the GAM models could explain 73.7%, 84.4%, and 84.5% of the deviations for the three predicted pollutants. For 90% of the sample data, the accuracy of the bioaccumulation prediction was 93.44% for volatile phenol bioaccumulation, and 90.16% for Cu and coliform bioaccumulation. The proposed risk assessment method could be extended for aquaculture applications that require monitoring of additional parameters to safeguard aquaculture quality and facilitate effective safety risk management.

Wireless Sensor Network (WSN) is applied widely in food cold chain logistics. However, traditional monitoring systems require significant real-time sensor data transmission which will result in heavy data traffic and communication systems overloading, and thus reduce the data collection and transmission efficiency. This research aims to develop a temperature Monitoring System for Frozen and Chilled Aquatic Products (MS-FCAP) based on WSN integrated with Compressed Sending (CS) to improve the efficiency of MS-FCAP. Through understanding the temperature and related information requirements of frozen and chilled aquatic products cold chain logistics, this paper illustrates the design of the CS model which consists of sparse sampling and data reconstruction, and shelf-life prediction. The system was implemented and evaluated in cold chain logistics between Hainan and Beijing in China. The evaluation result suggests that MS-FCAP has a high accuracy in reconstructing temperature data under variable temperature condition as well as under constant temperature condition. The result shows that MS-FCAP is capable of recovering the sampled sensor data accurately and efficiently, reflecting the real-time temperature change in the refrigerated truck during cold chain logistics, and providing effective decision support traceability for quality and safety assurance of frozen and chilled aquatic products.

With continuous rise of table grapes consumption and increased public awareness of food safety, the quality control of grapes in storage after purchase is not sufficiently examined. Home storage constitutes the last and important stage in grape supply chain. Literature review shows that few researches on grape quality focus on the home storage stage compared with numerous researches reported on the quality control during postharvest and transportation process. This paper reports the performance evaluation of grape quality at home storage and consumers’ satisfaction using integrated sensory evaluations. The internal attributes, including Texture, Taste and Odor of the table grapes and the appearance indices, Color and Cleanliness are examined. Key results show that during home storage, all the internal attributes decrease rapidly as time goes on, and cleanliness and color appear to be deteriorating in a lower speed. A comprehensive quality index was created to measure the quality of table grape which has high correlation with the Overall acceptability perceived by consumers.

Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar&ndash, acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch &ldquo, Spring Belle&rdquo, ) were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.

In the process of agricultural production, the yield of field crops is affected by the interaction of various factors. This relationship between soil nutrients, fertilization, and yield is very complicated. It has strongly non-linear and black box characteristics and is difficult to quantify with traditional analytic methods. This paper proposes a method for the precision fertilization of maize based on the wavelet-BP neural network. Firstly, the existing “3414″ experimental data was chosen and interpolated as the performing data for the modeling. Then, the general condition of low frequency and details of high-frequency of the yield were calculated via the wavelet decomposition and reconstruction method. After that, the wavelet-BP neural network was established. Three different BP neural sub-networks for each component of the yield after wavelet analysis were established, and the output of each sub-network was summed to obtain the predicted corn yield. The results show that the model that combines wavelet analysis with the BP neural network achieves better performance than the traditional BP neural network, Support Vector Machine (SVM), and Random Forest regarding accuracy and stability, which indicates the feasibility of this method. Finally, the best fertilizer amount that achieves the maximum yield or maximum profit is calculated based on nonlinear programming. The proposed method of the wavelet-BP neural network provides a new technique for precision fertilization research and enriches the existing fertilization system. The application of precision fertilization based on the wavelet-BP neural network has important practical significance that guides and increases maize production, while reducing production cost and agricultural pollution. At the same time, it also provides a way to solve similar problems in the entire field of cleaner agricultural production.

Low-carbon production is one of the dominating issues in the sustainable development of the food industry with high energy consumption, especially in the table grapes cold chain. The aim of this paper is to propose a profit maximization strategy of table grapes cold chain by integrating the carbon footprint to improve the low-carbon production and sustainability of the cold chain system. The carbon footprint was evaluated by life cycle assessment (LCA) in entire cold chain system of table grapes, and the economic order quantity (EOQ) model was used to develop the profit maximization model with minimal carbon footprint and to maintain the optimal balance between stock and cost. The profit optimization performance, the sensitivity performance and the influencing factors of the decay rate, the carbon emission price, and the distance and carbon emission coefficient in refrigerated transport were analyzed according to the profit maximization model and the inventory data in actual cold chain investigated. The sensitivity performance analysis illustrated that the selling price had the highest sensitivity, and the carbon emission coefficient in storage had the lowest sensitivity. The comprehensive analysis results indicated that there is an optimal combination point between the economy and environment in actual cold chain, which not only reduced the carbon emission, but also had minimal impact on the profit in cold chain. The enterprises should integrate the carbon footprint cost into the profit maximization once the carbon emission tax is levied. The proposed strategy of the profit maximization with carbon footprint constraint is also suitable for improving profit maximization of other low-carbon supply chain applications.

Implementation of food traceability systems in China faces many challenges due to the scale, diversity, and complexity of China's food supply chains. This study aims to identify critical success factors specific to the implementation of traceability systems in China. Twenty-seven critical success factors were identified in the literature. Interviews with managers at four food enterprises in a prestudy helped identify success criteria and five additional critical success factors. These critical success factors were tested through a survey of managers in 83 food companies. This study identifies six dimensions for critical success factors: laws, regulations, and standards; government support; consumer knowledge and support; effective management and communication; top management, company wide, and vendor support; and information and system quality.

Temperature monitoring, shelf-life visibility and Least Shelf-life First Out (LSFO) stock strategy are important contents in perishable food cold chain logistics for both cold chain managers and workers in order to reduce quality and economic losses. This paper illustrates a wireless sensor network (WSN) based integrated Cold Chain Shelf Life Decision Support System (C2SLDS) designed for perishable food product cold chain management. The system is implemented based on the WSN and time temperature indicator (TTI) features. Compared with traditional cold chain management methods used before, the C2SLDS not only bridges the information gap which exists between different cold chain phase enterprises and provide a seamless information flow along the whole chain but also enables cold chain enterprises to predict perishable food's shelf-life and helps make a smart LSFO strategy to reduce the quality and economic loss. System test and evaluation shows that the infield radio transmission is reliable and the whole system meets most of the users' requirements raised in system analysis.

Tricholoma matsutake (T. matsutake) growing in Tibet is very popular for its high economic and medicinal value, but fresh T. matsutake has an extremely short shelf life. The shelf life of T. matsutake is complex, influenced by product characteristics, surrounding environmental conditions, and spoilage development. The objective of this work was to study the quality characteristics of fresh T. matsutake during its shelf life period in modified atmosphere packaging (MAP) conditions and establish its remaining shelf life prediction models in a cold chain. In this study, we measured and analyzed quality indicators of fresh T. matsutake, including hardness (cap, stipe), color, odor of sensory characteristics, pH, soluble solids content (SSC), and moisture content (MC) of physical and chemical characteristics under the temperature condition of 4 &deg, C and relative humidity (RH) of 90%. The sensory evaluation results showed that the odor indicator in sensory characteristics was more sensitive to the freshness of T. matsutake. The changes of pH, SSC, and MC were divided into three periods to analyze the physiological changes of T. matsutake. The cap spread process could affect the changes of pH, SSC, and MC in period S1, and they changed gradually in period S2. In the period S3, they changed complicatedly because of deterioration. The remaining shelf life prediction model of T. matsutake was established by the back propagation (BP) neural network method to quantify the relationship between the quality indicators and the remaining shelf life. The shelf life characteristics are complex, which were optimized by correlation analysis. Significant benefits of this work are anticipated on the transportation and preservation of fresh T. matsutake to the market and the reduction of its losses in the postharvest chain.

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.

This paper theoretically analyzes the quality changes of aquatic products by the temperature environments according to the types of temperature fluctuations. First the temperature fluctuation is divided into three types of temperature environments for discussion during the transportation in the aquatic cold chain, by which the temperature model is put forward for each type. And then quality evaluation models are established respectively for the three temperature fluctuating environments. Finally, experiments are given, which show the effectiveness of the models.

The learner’s acceptance of e-learning systems has received extensive attention in prior studies, but how their experience of using e-learning systems impacts on their behavioural intention to reuse those systems has attracted limited research. As the applications of e-learning are still gaining momentum in developing countries, such as China, it is necessary to examine the relationships between e-learners’ experience and perceptions and their behavioural intention to reuse, because it is argued that system reuse is an important indicator of the system’s success. Therefore, a better understanding of the multiple factors affecting the e-learner’s intention to reuse could help e-learning system researchers and providers to develop more effective and acceptable e-learning systems. Underpinned by the information system success model, technology acceptance model and self-efficacy theory, a theoretical framework was developed to investigate the learner’s behavioural intention to reuse e-learning systems. A total of 280 e-learners were surveyed to validate the measurements and proposed research model. The results demonstrated that e-learning service quality, course quality, perceived usefulness, perceived ease of use and self-efficacy had direct effects on users’ behavioural intention to reuse. System functionality and system response have an indirect effect, but system interactivity had no significant effect. Furthermore, self-efficacy affected perceived ease of use that positively influenced perceived usefulness.

This paper presents a method to extract color and texture features of an image quickly for content-based image retrieval (CBIR). First, HSV color space is quantified rationally. Color histogram and texture features based on a co-occurrence matrix are extracted to form feature vectors. Then the characteristics of the global color histogram, local color histogram and texture features are compared and analyzed for CBIR. Based on these works, a CBIR system is designed using color and texture fused features by constructing weights of feature vectors. The relevant retrieval experiments show that the fused features retrieval brings better visual feeling than the single feature retrieval, which means better retrieval results.

Swine-Vet is a kind of a web-based expert system for swine disease diagnosis and has been promoted in north of China past 3years. The few issues relevant with low diagnosis efficiency and error have arisen during the system test and promotion due to the limitation of previous adopted reasoning techniques and immaturity of diagnosis domain knowledge. This paper proposes a hybrid multimodal reasoning mechanism to resolve the swine intercurrent diseases and improve the system diagnosis efficiency. The framework consists of two modules: one is the forward diagnosing module, information fusion method based on evidence theory is used to solve the barriers of syndrome in swine diagnosis field, and the other is the backward confirming module, set pair analysis (SPA) is adopted to resolve evidence conflict and distinguish uncertain and unknown symptom information during the process of diagnosis. Then, in order to demonstrate this model, a case study is given. Finally, the benefits and challenges of the hybrid multimodal reasoning application in expert system are discussed.

Agricultural domestic support is a sensitive issue for all countries producing agricultural commodities. To simulate the dynamic influence of changes in agricultural subsidies on non-agricultural sectors, this paper improved the Indian Storm models by embedding the labor force module based on the new Austrian model. The simulation result showed that a steady increase in the rate of producers' subsidy had little effect on capital flows, non-agricultural sectors and the rate at which agricultural surplus labor force transfers to non-agricultural industries. The non-agricultural industry should benefit from an increase in the rate of producer subsidies, as total consumption expenditure by non-agricultural industry is reduced. Producer subsidies facilitate transfer of labor force from agriculture to the light manufactures sector the most, the fertilizer and service sectors second, and the heavy manufactures sector the least.

This paper, from innovation adoption perspective, investigates people's perceptions and attitudes toward adopting e-learning to explore the key factors affecting the e-learning adoption behavior in China. Based on Rogers' innovation adoption theory, 33 factors of perceived innovative attributes will be quantificationally analysed to test the relationship between the perceived innovative attributes and adoption intention of e-learning by an analytic hierarchy process (AHP). The result shows that some factors of perceived innovative attributes, such as cost, quality, agility, schedule control, certification of degree, personal demands and so on, have more influence on peoples' adoption of e-learning.

Expert systems (ESs) were introduced more than two decades ago, but their effectiveness and success are still in debate. This paper attempts to make a contribution to the better understanding of ES applications from a knowledge transfer perspective. This paper argues that an ES application is knowledge transfer that uses Information and Communication Technologies (ICT). Underpinned by knowledge transfer theories and through a series of empirical investigations of ES projects in agriculture, this study recognises the importance of human interactions in the ESs implementation process. Based on the evidence collected, a number of key players are examined. They are knowledge provider/sender, knowledge engineer, knowledge extensionist and knowledge recipient. This paper represents a first attempt to acknowledge the role of a knowledge extensionist in the ICT-based knowledge transfer process. The name ‘extensionist’ is borrowed from earlier literature and describes an actor whose role is an intermediary in supporting transferring knowledge towards the knowledge user. Findings demonstrate the significant contributions made by extensionists towards the success of ESs applications. It is argued that the rigidity and limitations of ESs in ICT-based knowledge transfer can be significantly reduced with the involvement of close human interactions with the knowledge recipient.

Fish disease is becoming the major factor damaging the sustainable development of aquaculture and the quality security of aquatic product in China. Many researchers provide assistances and advice in fish disease diagnosis and appropriate treatment measures. However, the risk of misdiagnosis, incorrectly treatment or over-treatment also exists due to carrying out after disease outbreaks. So it is necessary to develop an early warning system. Taking into account that fish disease outbreak is mainly caused by suboptimal water quality in north of China, the best way is to develop decision aids tool that can assess and early warning the risk of fish disease based on water quality management. This paper introduces a knowledge-based early warning system for fish disease via water quality management. The architecture, the process for knowledge engineering, main components and their functions, especially a series of water quality management mathematic models are described. At present, the system is in pilot in the city of Tianjin in China. The stage achievements in developing the intellectual system of fish disease early warning are summarized.

Domain concepts acquisition is a key step of knowledge acquisition. In this paper we propose a new method for computing the relationship of domain concepts of China's vegetable supply chain using WordNet and Locally Linear Embedding (LLE) Arithmetic. WordNet is used to construct the high dimension description of the domain concepts and LLE is used to compute the relationship of concepts in low dimension space. The concepts description in the high dimension is in accord with human cognition and the low dimension relationship can reflect the degree of correlation of domain concepts clearly. A computer can deal with domain concepts directly after dimensional transformation. The experiment proves the efficiency of this method. The method can be expanded to deal with other examples of domain concepts acquisition.

Because the traditional FTP search engines usually adopt centralised spiders to collect data, insufficient temporal effectiveness is their major demerit. For solving this problem, this paper presents an efficient hierarchical FTP search engine model that deploys the spider agent on the node host of some specific network for collecting file data of FTP servers. The key technologies involve a regional responsibility mechanism, a search mechanism based on the asynchronous retrieval technology and a PAT Tree storage mechanism. The simulation shows that the responding time is less than that of the traditional system and the temporal effectiveness arrives at the application level. In addition, the test results show that the architecture has a good scalability.

This study examines perceptions and attitudes towards adoption of mobile commerce from an innovation adoption perspective. Six variables of a theoretical framework developed based on the TAM model are tested using a cross‐sectional survey in China's rural areas. The findings indicate that the six variables, perceived usefulness, perceived ease of use, personal innovativeness, compatibility, perceived reliability, cost, all significantly affect user attitude to adoption of m‐commerce. The study helps providers to gain valuable insights into the key factors influencing uptake and to offer more effective, relevant and successful m‐commerce products.

Resolving inventory and location problems is the most important and fundamental tactical decision in the initial stages of developing a distribution network design. This is especially true for a logistics system dealing with perishable food. To minimise both the total inventory transportation cost of fresh agri‐products and also waste in the supply chain, the evolution of the food supply chain structure is analysed and an inventory location allocation model is presented describing the real‐life problem. The key issues are to describe food demand distribution and to determine the balance of transportation and inventory cost. To improve the quality of the solution, a local search is embedded in the two‐grade delayed particle swarm optimisation. Experimental results demonstrate the algorithm can effectively resolve conflict between different costs and improve development decisions regarding Beijing's perishable food distribution centres.

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 people's 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 people's adoption of e‐learning.

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.

The paper presents a method that addresses the problem of the amount of green space required for the environment areas of lower biodiversity by the ecological element threshold method. The original habitat had been highly disturbed by human activities in most developing countries. Taking the population carrying capacity, the balance of carbon–oxygen, and the supply–demand equilibrium of water resource as a group of conjugate restriction factors of green space planning, it quantifies the total amount of green space required to keep the ecological system in balance for the town of Shaliuhe, Hebei Province as the case study. The results show that the main restrictive factor at Shaliuhe town is the imbalance between the supply and demand for the water resource, 89.34% of which is used in agriculture. Therefore, the effects of various ecological improvements are calculated for the years 2005, 2010 and 2015. This case study could be used as a model for the planning of other towns on the northeast China plain to improve the environment, ecology and sustainability. Similarly, the Chinese scenario might provide a useable reference to other developing countries.

Purpose – The purpose of this paper is to estimate the technical efficiency of Chinese table grape wholesalers and subsequently to examine the degree to which the calculated efficiency correlates with a set of explanatory variables. Design/methodology/approach – A two-stage procedure is applied in this paper. First, a non-parametric data envelopment analysis (DEA) technique is applied to investigate the degree of technical efficiency for Chinese table grape wholesalers. Second, Tobit regression is used to analyze the factors influencing technical efficiency. Findings – Research results reveal that the mean technical efficiency of the sample is 0.544 and 0.860 under constant returns scale (CRS) and VRS assumptions, respectively, and the scale efficiency (SE) is 0.620. The variables of experiences, number of grape varieties on sale, daily selling volumes and fixed sale ratio have a significant effect on technical efficiency, while the other exogenous variables do not affect the efficiency in any significant way. Research limitations/implications – The findings of this research are specific to table grape wholesalers in China, however, the method used in this study is transferrable and applicable to the study of similar problems in others countries. Originality/value – This research has yielded some interesting and original insights into the efficiency level of Chinese grape wholesalers and the factors that influence the level of efficiency. The findings have practical implications for Chinese agriculture policy makers, and are able to stimulate further research of a similar type in the international agricultural research community.

Adoption of IT innovations is attracting increasing attention. Researchers are particularly interested in factors that affect the adoption of IS and IT innovations. Innovation diffusion theory is used frequently to evaluate the effect of perceived innovation attributes and the adoption of innovations. Nevertheless, explanatory power of perceived innovation attributes varies across different innovations. Given the importance of online e-payment to the further development of e-commerce and its importance as a payment innovation, this research examines the adoption of online e-payment by business enterprises using Rogers’ relational model of perceived innovation attributes and rate of adoption. The findings indicate that only perceived compatibility has significant influence on online e-payment adoption of Chinese companies. It is hoped that this research can help other researchers with related statistical procedures and analytical steps in their study of IS/IT adoption using innovation diffusion theory.

This paper presents a research attempt to develop and evaluate a Web-based tele-diagnosis system for fish diseases. Fish disease diagnosis is a complicated process and requires high level of expertise. A tele-diagnosis system T-VET is being developed and tested in China. T-VET is the key sub system of a Web-based expert system for fish disease diagnosis. This paper looks at why a tele-diagnosis system is needed and what are the advantages and difficulties in developing and using such a system. The system's architecture and components are described. A user-centered approach was adopted throughout the development process to ensure the system would meet users' needs and will be accepted. As a result, the system functions were emerged and improved by users' requirements. The experience and feedback from testing and evaluating the system with potential users are discussed. Some valuable insights into the use of the Internet as a tele-diagnosis tool in aquaculture are highlighted.

Ammonia concentration is the major parameter to evaluate livestock breeding farms atmosphere quality and it also is regarded as the key indicator to describe the production of livestock breeding farms. Based on the oxidation characteristics of ammonia, this paper presented a new intelligent detecting instrument, the intelligent ammonia sensor, for the measurement of ammonia concentration, which used the microcontroller STM8L152 as the key control module. However, the TEDS module, which is useful to self-identification, self-diagnosis and self-calibration of ammonia sensor, is in the flash of the STM8L152 and only if the STM8L152 conveys the warning signal to the alarm module, will the alarm module starts the worker loop.

Edge geometric measurement analysis is an important method of image understanding and portraying the target feature. In this paper, we compress 17 interrelated shape descriptors which are based on edge geometric measure into 6 independent components, and discuss their meanings by using principal component analysis. The analyses in this article provide guidance for the shape feature optimization and accurate identification for greenhouse strawberry leaves images successfully.