Citrus is one of the most widely cultivated fruit in the world. However, citrus diseases are becoming more and more serious, which has caused substantial economic losses to citrus growers. With the rapid developments of mobile device, mobile services computing play an increasingly important role in our daily lives. How to develop an intelligent diagnosis system for citrus diseases based on mobile services computing and bridge the gap between citrus growers and plant diagnostic experts is worth studying. In this paper, we build an image dataset of six kinds of citrus diseases with the help of experts and realize an intelligent diagnosis system for citrus diseases by constructing the simplified densely connected convolutional networks (DenseNet). The system is realized using the WeChat applet in the mobile device, with which users can upload images and receive diagnostic results and comments. The experimental results show that the recognition accuracy of citrus diseases exceeds 88% and the predict time consumption has also been reduced by simplifying the structure of the DenseNet.

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A Smart Mobile Diagnosis System for Citrus Diseases Based on Densely Connected Convolutional Networks

At present, green plants exist in all aspects of our lives. And statistics shows that the pest species of green plants is very large. Thus accurate and rapid diagnosis is regarded as an essential component of green plant protection. Moreover, we couldn’t find relevant information easily. That is why pest diagnosis is difficult and inefficient for technicians and farmers. In view of the above, the expert systems have been widely used in pest identification. However, most of traditional expert systems for assistant diagnosis of green plant pests are based on dichotomous structure. They are not flexible enough and only equal the electronic dichotomous keys. Compared with dichotomous structure, the system based on multi-branch structure has more advantages for accurate and rapid diagnosis. This paper describes the design and development of a web-based green plant pest expert system as part of Tianjin science and technology cooperation project. Based on user needs, Tianjin Planting Pest Assistant Diagnosis System (TPPADS) was developed with ASP.NET, C# and Microsoft SQL server 2008 database. It can show many features simultaneously. Meanwhile, data maintaining is also very easy and simple as same as the Microsoft Windows Explorer. The system included about more than 300 species of green plant pests. Diagnosis knowledge was obtained from Tianjin Institute of Plant Protection. TPPADS can be used as a diagnosis tool and information database both for plant protection professionals and farmers. We believe its application prospect should be well.

/pdf/tppads-an-expert-system-based-on-multi-branch-structure-for-16rgfbxq17.pdf

TPPADS: An Expert System Based on Multi-branch Structure for Tianjin Planting Pest Assistant Diagnosis

This paper describe the design and implementation of the VES,an expert system for diagnosis vaginitis based on the rule reasoning.Some problems in this system and methods while implementing are discussed according to the feature of vaginitis.This paper presents its architecture,knowledge representation,reasoning and the key technology in detail.Diverse skills connecting database,artifioial intelligence,as well as vaginitis diagnosis are to tally integrated and this exper system is valuable to realize vaginitis disease intelligent diagnosis.

VES:Design and Implemention of an Expert System for Diagnosing Vaginitis

WebGIS has many advantages such as widely recognition, abundant users, platform crossing, low average cost and easy to use for the final users. Now, it has been widely used in all walks of life. This paper introduced the WebGIS technology first, and then introduced the functions of the "Kunming Shilin Taiwan farmers entrepreneurial park" information platform and the way to realize these functions. Finally we use WebGIS technology to develop the geographic information visualization platform based on ArcGIS and realize the visual management function of the park-based infrastructure (including enterprises, roads, pumping stations, water storage Pool, water supply pipes, video monitoring points and other geo-features) based on map. For now, the platform has been in operation for the management of "Kunming Shilin Taiwan Farmer Pioneering Park".

https://www.dpi-proceedings.com/index.php/dtcse/article/download/17513/17020

Application of WebGIS Technology in Information Management of Agricultural Science and Technology Park

Recently, there are many problems in software laboratory course management. It is, particularly, widespread that student play video games or do other things which are unrelated to the course. However, most of present monitoring software are all based on the operation of teachers, rather than a real-time system, which is very inconvenient. In order to carry out the laboratory course management better and help teachers to monitor and analyze students' behaviors of operating computer, we design and implement a model of Self-adaptive Monitoring and Analysis System (SMAS) for students' behaviors in laboratory course. For monitoring, we implement a module which uses the technology neural-network-based expert system, with which we can monitor the students' behavior real-time and self-adaptively. For analysis, with the methodologies such as Factor Analysis, Cluster Analysis, and Discriminant Analysis, we propose and implement an evaluation algorithm, which can evaluate the students' performance in a course quantificationally and obviously.

A Self-Adaptive Monitoring and Analysis System for Students' Behaviors in Laboratory Course

In this paper, on the basis of full investigation and a lot of comparison with expert systems at home and abroad, introducing Web technology, WebGIS and BP neural network technology into expert system and taking full use of their respective advantages, we have designed and developed a remote diagnosis and control expert system for citrus agricultural diseases and insect pests based on WebGIS and BP neural network. We put emphases on the design of forward inference engine based on neural network and reverse inference engine based on rule system. In the whole expert system, neural network serves as a forward inference engine for the expert system. It accepts standardized raw data input of the diseases and insect pests and gives the diagnosis conclusion after reasoning process. Reverse reasoning is made by rule system, which is going to verify the diagnosis conclusions provided by neural network. In this way, the reasoning speed of entire system and correct diagnosis rate are greatly increased. At the same time, the rule system provides control methods for diseases and insect pests according to the diagnosis conclusions and also provides its geographical distribution information based on GIS. The experimental results show that the expert system has an accurate and reliable performance, so it has a good promotion prospects for applications.

Yu Guangzhou

Papers

Remote Diagnosis and Control Expert System for Citrus Agricultural Diseases and Insect Pests Based on BP Neural Network and WebGIS