Study On The South Grape Expert System Using Artificial Neural Network And Machine Vision

Cultivation technique system based on growth and development regular pattern of grape is a complicated system. The expert who is farmilar with this system plays a critical role in the establishment and development of agriculture industrialization. However, during the development of agriculture industrialization, the healthy development of industry will be strongly impacted by the shortage of these experts or providing the service behind schedule. The expert system designed through computer technology is a very important channel to solve this problem.This paper took the whole process of grape development in south as the research object, did the analysis and research for the cultivation management, grape diseases diagnostic method and the relationship of endoplasm parameters and images information for the whole process of grape development, had designed a south grape expert system based on neural network and machine vision. Mainly focused on the structure of south grape expert system, consultation and decision aids of cultivation management, grape diseases diagnostic and nondestructive measurement before harvest. The purpose was in the service of the cultivation and production of grapes better through these design system in the south.The main research contents included:1. Designed the south grape expert system platform based on neural network and machine vision, and made used of object-oriented method, .Net and database technology to constructed the expert system platform, meanwhile, designed the architecture, functional structure and module designs based on this platform.2. Constructed the subsystem of cultivation management, and designed the grape cultivation management expert decision system and information consultation functions. This subsystem provided the users with convenient and practical management guidance and intelligent decision-making of cultivation management services.3. Designed and constructed the grape diseases diagnostic subsystem. This subsystem took eighteen typical grape diseases as the research object, designed the grape knowledge base and factual database. With the RBF artificial neural network model, designed the diseases diagnostic knowledge database and inference engine. Have done the analytic demonstration of network architecture and training method. The validity of this diagnostic module was validated by experiments4. Designed and constructed the nondestructive measurement before harvest subsystem of grape under natural environment. The experimental materials were the two years grape cultivars of Ruby seedless and Red Globe, which belong to V. Vinifera. Collected and tested the images information and endoplasm parameters during the whole progress of grape development. Have done the research for the images segmentation and feature extraction method under the natural environment, and combined with correspondent endoplasm parameters. The combined neural network model was designed for nondestructive measurement before harvest. After training for this combined neural network, the subsystem could effectively and accurately do the nondestructive examination for Ruby seedless and Red Globe grape.This article has done innovative work includes the following.To consider the characteristics of grape in south, combined with the practical needs of grape production, designed the prototype of the south grape expert system based on neural networks and machine vision technology. The domain expert knowledge and advanced computer technology were used in this prototype system.A good real-time, diagnostic accuracy of disease diagnosis model was proposed, to fix the complex and real-time problem for grape disease diagnostic. In this module, different inference rules were designed for typical and atypical grape diseases, so it has improved the real-time capabilities.For the feature extraction of images information issue under natural environment, a method which was based on the edge detection of images segmentation method and the frequency of color hue was proposed. Since the grape color was very similar with the background, the edge detection and the improved Hough transform method were selected to effectively partition image. Made use of the frequency statistics of the color hue of grape image to reduce the impact of angle-dependent and size during photographing and make it is easier for feature extraction.For the nondestructive measurement of grape, a combined artificial neural network module was proposed based on frequency sequence of images information and endoplasm parameters. After the pertinence analysis of endoplasm parameters, designed an artificial neural network which built the training sample with grape images information and pericarpial pigment parameters and another artificial neural network which built the training samples with pericarpial pigment parameters and total acid, total sugar, soluble solids of sarcocarp. Using this combined module, realized the nondestructive measurement according to grape images information. The experimental accuracy and precision met the requirements of an ideal after independent training and combined test.