Research Of Information Acquire And Identify Model For Apple Fruit Disease By Near Infrared Spectrum

Post harvest fruit diseases can be divided into a living bacterial diseases and physiological reason two categories: physiological diseases have a very good basis of previous studies, through the atmosphere, temperature and other methods can be effectively transferred control; bacterial disease of non-destructive evaluation With the exception of a small amount of electrical parameters based on characteristics of methods, very few studies, using near infrared spectroscopy for nondestructive evaluation has rarely been reported. Therefore, using fast near infrared spectroscopy, green, and other characteristics of non-destructive test to establish disease models Apple has important theoretical and practical value.Taking Apple fruit as the research object to post harvest diseases (ring rot and anthracnose) and near infrared spectroscopy to study the means, in the previous study on apple diseases on the basis of non-destructive evaluation were collected from the near-infrared spectral information Factors, sample selection and classification model parameters and model of disease areas such as research and development on this basis, near infrared spectroscopy-based disease identification software system Apple. In this paper, results are as follows:(1) Apple spectral factors of information acquisition. The same sample, the same location a total of 9 hours straight 53 proof of spectral acquisition, ASD's Field Spec 3 Spectrometer performance and stability; stray light only visible part of the spectrum showed that the effect in the near infrared band almost no effect; fruits of different Parts of the near infrared spectra of different samples Apple equator line spectral repeatability is good, reliability, and the pedicel and fruit quite different spectrum E; 0mm distance in the fiber optic probe of the radiation sources as generated A certain block, spectrum very different; in a distance of 2.5mm-12.5mm, the perspective of the bare fiber is 25°, corresponding to the lighting interface, Apple did not exceed the maximum height, so little change; more than 12.5mm After the incident Energy loss, the signal energy is reduced, the error becomes larger; different weight loss shelf life of apple changed significantly, due to internal water loss, the acquisition significantly different near-infrared spectroscopy; fruit of different color in the equatorial plane near infrared spectroscopy No significant difference between the near infrared and visible bands of significant differences, the actual detection of near-infrared surface color of the fruit can be ignored.(2) Sample selection and modeling parameters: with the best sugar content of the relevant degree of proof for the study sample by removing the anomalies can significantly improve the accuracy of the model; comparative analysis of the different spectral pretreatment methods, different smooth points, the number of different factors and different cross validation PLS modeling band Sugar Apple predicted quantitative prediction accuracy. Concluded that the best pretreatment method for the SNV and MSC; optimal smoothing of 3 points; interaction factor authentication is the best number is 3; modeling the choice of band for the entire near infrared region.(3) Building the identify model of Apple's disease. Taking the apple ring rot and anthracnose fruit as experimental subjects, the qualitative analysis PCA model can be controlled separately from health apples and infected apple, but low recognition rate among the two diseases. Using principal component analysis, the PCA principal component as an input parameter, use the nearest neighbor KNN, BP neural network classification algorithm, and the identification between these two diseases was increased to 85% and 90%.(4) To achieve near-infrared spectra based on Apple's disease identification, a software system was developed.