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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 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.

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.

Traceability system can effectively trace food quality and reduce safety scares. Recent researches on traceability systems in China focus on technology innovation, traceability system management, and determinants of traceability system implementation. This paper proposes four criteria to analyze strengths and limitations of the operating mechanisms. The result shows the operating mechanism of traceability system in Chinese agribusiness can be classified into three categories and none of operating mechanism of traceability system completely meets the agribusiness traceability service requirement, and each of the three operating mechanisms has its own strengths and limitations. It is suggested that an integrated mechanism is needed to implement traceability system in agribusiness.

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 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.

Aquaculture has moved from conventional open systems to high density and highly productive land-based recirculation systems. Consumers have increased consumption of fish and fish products due to recognition of their nutritional value along with social progress and the improvement of living standards. A traceability system is considered as an effective tool to guarantee safety in fish products and improve the supply chain transparency. This paper developed a Wireless Sensor Network (WSN) based Traceability System for Recirculation Aquaculture (RATS). System tests shows that the WSN-based traceability system has comparable data accuracy and advantage of easy installment and configuration.

The system of Case Based Reason is researched in this paper, a kind of genetic algorithm is proposed to solve the problem of feature vector space, and proposes an approach of integration of CBR AND RBR, an example about fish disease diagnose inferring integration model is given.

Dissolved oxygen (DO) concentration plays a very important role in fish life and aquaculture, but DO prediction is very difficult. So a decision support system for DO prediction based on fuzzy model and neural network was attempted. The paper was based on vast monitored data, every day detecting for two years, in aquaculture pond in North China for two years. This is a preliminary attempt towards a wider use of Artificial Neural Networks in the management of aquaculture water quality. It proposes a model to be used effectively in prediction of DO concentration in aquaculture. This is really a crucial task, especially during the long dry summer months. The prediction of potential risk due to low DO is also very important. This data volume was divided in the training subset comprising of 106 cases and in the testing subset containing 26 cases. The input parameters are sunlight, wind speed, temperature, water temperature, air pressure, pH value and NH-NH3. Consequently three structural and seven dynamic factors are considered. After several and extended training-testing efforts a Modular Artificial Neural Network was determined to be the optimal one.

In this paper, genetic algorithms and machine learning theory and method of knowledge acquisition for expert system structure is proposed to solve the "bottleneck" problems in the traditional machine learning methods on the basis of AQ, the model of knowledge acquisition based on GA is proposed and its application is used in the Fish Disease Diagnosis reasoning system, the rules is accessed to fish disease diagnosis. Fish Disease Diagnosis and the problems of knowledge accessed into combinatorial optimization are solved.

Efficient knowledge management is one of the central challenges for the successful construction and following use of expert system. This paper presents a framework of knowledge management in Fish-expert. The main benefit is high improvement and facilitate in the precision. At present the version of fish-expert V2.0 has been successfully used for some areas in north of China. Compared with the first version, the amount of rules had decreased from 1890 to 312 and the reasoning speed and veracity had been promoted from 1 second to 0.5 second and 85% to 95 % or more separately. The result indicates that Fish-expert performance is at least as good as the average of the human experts but can be promoted in the parasite identifies.

With the recent rapid progress of computer technology, researchers have attempted to adopt artificial intelligence and computer networks to develop computer-aided tutoring systems. Meanwhile, some have also attempted to develop more effective programs to test and enhance the learning performance of students. This study surveyed the current theoretical basis and design principle and proposed a web-based knowledge aided tutoring system for vegetable supply chain. The main idea of this paper was focused on the interactive design and feedback control. The proposed concept was illustrated by the functions of chat system, discussion section, questionnaires and online learning record, etc.

This paper proposed a new method to acquire vegetable supply chain domain knowledge from three semantic extension layers. For a single domain concept, qualitative reasoning and quantitative reasoning are used to reach the first semantic extension layer; for more than two domain concepts, Advanced Similarity Measure (ASM) arithmetic is adopted to reach the second semantic extension layer. These two semantic extension layers are achieved internal of vegetable supply chain ontology model. WordNet and Locally Linear Embedding (LLE) arithmetic are adopted to reach the third semantic extension layer, which is achieved external of the ontology model. The experiments have proven the efficiency of this method. The method can be expanded to domain concepts acquisition of other fields.

In this paper, a kind of 3-layer index inverted table is proposed, which differs from the usual one for the introduction of the additional layer for semantic items. We take advantage of some semantic markups and convert the content of documents to a set of ontology terms. The index tables for search engine obtains those converted terms, which match with the terms from the query, in that the precision of matching can be improved effectively.

This paper offers a qualitative study on the process of knowledge transfer that focuses on the expert system development. Inter-disciplinary approach is applied to review, analyse and develop a framework for the process. A framework of knowledge transfer process is proposed as having: (1) interactive transfer process: knowledge acquisition, representation, assimilation and dissemination; and (2) a dynamic knowledge conversion and creation process for the explicit and tacit knowledge. A case study demonstrating the development of a group of aquaculture disease diagnosis systems is empirically studied for the verification of the process. As a result of the study's findings, the framework is proposed with its validity. The related implications and limitations of the study are also given.

Fish disease diagnosis is a complicated process and requires high level of expertise, an expert system for fish disease diagnosis is considered as an effective tool to help fish farmers. However, many farmers have no computers and are not able to access the Internet. Telephone and mobile uses increase rapidly, so, the provision of call centre service appears as a sound alternative support channel for farmer to acquire counseling and support. This paper presents a research attempt to develop and evaluate a call center oriented Hybrid disease diagnosis & consulting system (H-Vet) in aquaculture in China. This paper looks at why H-Vet is needed and what are the advantages and difficulties in the developing and using such a system. A machine learning approach is adopted, which helps to acquire knowledge when enhancing expert systems with the user information collected through call center. This paper also proposes a fuzzy Group Support Systems (GSS) framework for acquiring knowledge from individual expert and aggregating knowledge into workgroup knowledge by H-Vet in the situation of difficult disease diagnosis. The system’s architecture and components are described.

There are three kinds of uncertainty in the process of fish-disease diagnosis, such as randomicity, fuzzy and imperfection, which affect the veracity of fish-disease diagnostic conclusion. So, it is important to construct a fish-disease diagnostic model to effectively deal with these uncertainty knowledge's representation and reasoning. In this paper, the well-developed parsimonious covering theory capable of handling randomicity knowledge is extended. A fuzzy inference model capable of handling fuzzy knowledge is proposed, and the corresponding algorithms based the sequence of obtaining manifestations are provided to express imperfection knowledge. In the last, the model is proved to be effective and practicality through a set of fish-disease diagnostic cases.

This paper presents the comparableness of SVM method to artificial neural networks in the outlier detection problem of high dimensions. Experiments performed on real dataset show that the performance of this method is mostly superior to that of artificial neural networks. The proposed method, SVM served to exemplify that kernel-based learning algorithms can be employed as an efficient method for evaluating the revegetation potentiality of abandoned lands from coal mining activities.