| Crop abnormality of growth and development (CAGD) is one of the most important constraints on food production and economic efficiency in China. Affected by global climate change, crops planting structure and agriculture domain structure are adjusting obviously while resource-constrained situation is being more severe. These factors combined effect of the CAGD in the influence frequency, severity and extent, and occurrence of CAGD shows a constantly aggravate trend. Accurate and timely diagnosis of CAGDs and the predisposing factors play a key role in the decision-making of reasonable and effective control measures, and is an effective way to solve CAGD problems. Currently, the diagnostic techniques of CAGDs are most concerned about by the agricultural producers and grassroots agricultural service technicians, but also are the most frequently consulted questions in countryside.In order to solve the problem that frequent occurrence of CAGD is in sharp contradiction with the serious lack of agriculture experts, numerous studies using modern information technology to realize intelligent diagnosis of CAGD. The further researches, which establish a more comprehensive, intelligent and popular decision-support system for CAGD prevention and management, must be improving the diagnostic capabilities of expert systems through building novel inference mechanism and expanding the knowledge base. This is currently the most efficient solution for controlling CAGDs.In this study, we mainly focuses on three key techniques in intelligent decision-support system, respectively conceptual modeling of CAGD, diagnosis reasoning mechanism based on Context-aware computing, and the development of the utility application program for prevention and management of Maize growth and development abnormalities. The main contents of research can be as following:?. According to the occurrence features and knowledge traits of CAGD, the research content of domain knowledge is confirmed. An organization representation of CAGD based on ontology is proposed. Meanwhile, an ontology diagnosis knowledge model of maize growth and development abnormality has been built, including 40 kinds of maize diseases,57 species of agricultural pests,12 kinds of nutrient deficiency symptom,7 kinds of agro-meteorological disasters conditions and other related diagnosis knowledge.?. Through analyzing the characteristics from the domain knowledge of CAGD prevention and management, the ontology base of diagnosis knowledge is retrieved and matched. We propose a context inference-based intelligent system aimed at processing context information in agricultural production activities. Thereby system exploits both the locating and time information which are collected in smartphones and fuse them to improve the decisions and actions involved in prevention and management decision-making.?. An intelligent diagnosis system of maize abnormality of growth and development is developed on Android platform. Java and SQLite technology are adopted to realize diagnosis reasoning application program for maize abnormality of growth and development. Pest and agro-meteorological disasters conditions diagnosis are mainly based on classical harm symptom photograph. And disease and nutrient deficiency diagnosis are mainly based on binary-tree rule reasoning. The diagnosis interface and control interface are showed friendly, and designed clearly and used easily.The research is supported by the China Postdoctoral Science Foundation:the research of mobile smart device-based corn growth abnormalities recognition method (2014M561107):The intelligent decision-support system for prevention and management of Maize growth and development abnormalities based on mobile smartphone is developed, which is integrated some functions, such as diagnosis abnormality and spread protection technology. The creation of system has a good application value for corn producers in China. |
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