Classification Prediction Model Based on BP Neural Network

Abstract In this paper, a method of plant image processing combined with BP neural network was proposed to predict the disease degree of brassica napus. First, the initial image preprocessing, the roots of brassica napus. were used by Otsu threshold segmentation algorithm is combined with morphological open operation, segment the rape root image with better effect, in order to reduce image size inconsistent on the effects of extracting feature parameters, can be reconstructed images after segmentation, and then obtain the root image multiple characteristic parameters, Through chart correlation analysis to select the feature parameter information, with the characteristics of the rape root disease was positively associated with the degree of disease information to refactor pixel area and the longest diameter, finally establish the BP neural network, two characteristic parameters extracted as input, root disease degree level as the output, training network and prediction and comparison. The experimental results showed that the mean square error of the classification network for brassica olerica was 0.049, and the accuracy rate of the classification results predicted by the model was about 93%. The prediction effect was good and feasible.