Fusarium Micrograph Extraction And Recognition

Fusarium is a large genus in Fungi species,and they are varied and it distributes widely. Some of them are the pathogens which are the result of a variety of agriculture, forestry, plant disease, it can cause the wilting of crops, ear rot, decay and other diseases. Some of them are harmful to the crops seeds, it can produce toxin to harm the crops during its growth and development in the process of metabolism. It can cause the wilting or death of crops and affect the farm produce production and quality; sometimes it can lead to serious production dropped significantly. In addition, some types of Fusarium can grow in a variety of food and produce toxic metabolite; pigs, horses, donkeys and other livestock would show symptoms of poisoning after them eat it. The human being will show poisoning symptoms after they eat the mouldy food half an hour to one hour, the light may be vomiting, diarrhea and anti-feeding, the weight can cause death. Therefore, it is scientific and practical significance for the Fusarium Recognition and our life to study the Fusarium image extraction and recognition technology.The use of pattern recognition technology to Fusarium microscopic image classification and recognition is an important research topic in the field of microbiology. This paper makes some related studies of the Fusarium micrograph recognition system by image preprocessing, feature extraction, neural network technology, epecially pay more attention to Fusarium feature extraction and classification. This paper focuses on the following aspects:1. Fusarium micrograph enhancing and segmenting.Firstly, Changing Fusarium micrograph from RGB mode to grayscale mode, then mking use of histogram equalization and median filter combined to enhance useful information and reduce noise in the micrograph. Secondly, selecting morphology edge detection method based on the vector template for image segmentation. Finally we get more complete and clear edge by introducing Morphological approach based on template vector segmentation.2. Fusarium micrograph feature extraction.We do a wide range of surveys and in-depth study of traditional methods of characterization. According to the unique characteristics of sickle shape, we extract the features of Fusarium Binary micrograph (including geometrical and morphological 15 characteristics in total) and all the characteristics extracted are normalized. Optimization of feature selection, we select area, perimeter, long axis, short axis, Euler number, geometric moments, and a total of 12 feature parameters as input vector. The results showed that we can use BP neural networks to make feature classification with the characteristics extracted. 3. Fusarium micrograph feature classification.Above all, this paper analyzes the circumstances in which neural networks is use for pattern recognition, and select which kind of neural networks, and make decide to use BP neural networks to carry out the recognition for Fusarium micrograph. In the next place, we analysis the BP artificial neural network structural as well as network design parameters requirement and design based on BP neural network classifier In the MATLAB platform. In the end, we initially achieved the classification for Fusarium by optimizing BP neural network training targets, network structure and transfer function.