Yun Gao

Measurement of Chili Pepper Plants Size Based on Mathematical Morphology

Since chili pepper plant size directly reflects the state of plant growth, a method for pepper measurement of plants size was discussed here. Pepper plants were shot from above once per week in the greenhouse since being field planted in spring. The method of processing the pepper plant images was studied, in which the image segmentation of combination of color space and the image morphological operations were applied. And the major axis and minor axis of pepper plant, for describing the size of the plant, were calculated from single connected component in the image being processed. According to the method, a program for pepper plant size measurement based on MATLAB was developed. Experimental results have demonstrated that the method is more reasonable and accurate than artificial measure.

Discriminate of Moldy Chestnut Based on Near Infrared Spectroscopy and Feature Extraction by Fourier Transform

As near infrared spectra has the characters of multi-variables and strong correlations, to solve the problem, Fourier transform (FT) was used to extract feature variables of shelled chestnuts spectra. FT coefficients and the status of 178 chestnuts were selected as inputs and outputs of the back-propagation neural network (BPNN) classifier to build a recognition model. For comparison, principal component analysis (PCA) was utilized to compress the variables, which then was introduced as input of the neural network model. The results demonstrate that FT is a powerful feature extraction method and is better than PCA as a feature extraction method when employed together with BPNN. When the preprocessing method of standard normal variate transformation(SNV) was carried out and the first 15-point FT coefficients were used as the input, an optimal network structure of 15-6-1 was obtained, where discriminating rates of qualified chestnut, surface moldy chestnut and internal moldy chestnut in prediction set are 100%, 100% and 92.31%, respectively.