Abstract. The beef texture as marble is one of the important quality indexes of beef. Since the grading of beef marbling is largely determined by the subjective experience of the graders, there are inconsistencies and errors in judgment. Therefore, how to find objective and quantitative measure of the marbling abundance degree according to the grade standard of beef marbling has been one new study in the world meat science fields. We can use image analysis tools of Matlab to preprocess the image of beef marbling. Through data analysis, the percentage of image content marbled has been obtained, the detection model can be established through the construction of neural network, so it can lay the foundation for the prediction grade of the unknown kinds of beef marbling in the future.
A rapid and nondestructive way to measure protein and amylose content of rice was put forward based on near infrared(NIR) spectral technology. The NIR spectra were acquired from 13 varieties of rice with the wavelength from700 to 1100nm. The objectives of the present study were to establish forecasting model to find out the relationship between the absorbance of the spectrum and the main components of rice. By using the machine vision-based method, the rice appearance quality can be studied. On the basis of the evaluation criteria, 13 different kinds of rice were classified. And according to the usage of neural network, the detection model was established, so it can lay the foundation for the prediction grade of the unknown kinds of rice in the future.
Abstract. Plant disease has been a major constraining factor in the production of cucumber,the traditional diagnostic methods usually take a long time, and the control period is often missed. We take computer image processing as a method, preprocessing the images of more than 100 sheets of collected samples of cucumber leaves, using the region growing method to extract scab area of leaves to get three feature parameters of shape, color and texture. And then, through the establishment of BP neural network pattern, the model identification accuracy of cucumber leaf disease can reach 80%. The experiment shows that by using this method, the diseases of cucumber leaves can be identified more quickly and accurately. And the feature extraction and automatic diagnosis of cucumber leaf disease can be achieved.
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