Recognition of hazard grade for cotton blind stinkbug based on Naive Bayesian classifier

Abstract: Cotton, one of the most important economic crops in our country, always suffers a variety of pest during the whole process of planting. Blind stinkbug, which seriously affected the cotton quality and yield during BT cotton, is planted in large areas of the Yellow River and Xinjiang province in China. Traditional cotton blind stinkbug hazard ration identification method relies too much on experience, but recognition accuracy and recognition speed are low. In view of complex background of cotton blind stinkbug hazard region and the difficulty in segmentation and classification under natural conditions, an automatic classification method of Cotton blind stinkbug hazard level was proposed. On the basis of the classification standard of plant diseases and insect pests and hazard characteristics of cotton blind stinkbug, as well as the harm degree distribution of bug to cotton by artificial statistics, the cotton blind stinkbug damage grade was divided and the damage grade standard of bug to cotton was put forward. The processing steps of the cotton leaf image in different bug damage grade acquainted in natural conditions were as follows. Firstly, by using Q color component and Otsu segmentation method, the image background was divided. Secondly, in order to remove burrs after splitting, morphological opening operation and internal filling algorithm were used to deal with the segmented image, and the largest connected component was extracted, which can eliminate the influence of weeds. Thirdly, the disease regions of cotton were extract by H+a*+b* component and Otsu segmentation method based on blind stinkbug hazard cotton leaves. The adhesion cotton leaves were separated by Watershed segmentation method Forth, and extracted and selected contents including the color, texture and shape features of and cotton leaf hazard by blind stinkbug. In accordance with the principle of distinction and difference, color feature, texture feature and shape feature was the input indicators classifier. Based on the statistical results of color, texture and shape feature of bug damage image to cotton, R component, G component, B component, I1 component, S component and V component were selected as the color feature, Contrast and Correlation were selected as the texture feature, Pa value were selected as the shape feature. Finally, based on Matlab R2008 platform, combined with the bug feature variables and naive Bias classifier extraction, this method had the aim to distinguish the cotton blind stinkbug damage grade based on the cotton bug division of the harm grade standard. In this experiment, 120 cotton blind stinkbug damage leaves image with 6 levels were used for simulation, in which 60 images were the training set and the others were the validation set. Different bug harm level recognition experiment results showed that, the model has advantages in accuracy and speed with average rate of correct recognition as 90% and average operation time as 0.278 seconds, which was better than Support vector machine and BP neural network model. The proposed cotton blind stink bug hazard grade standard can provide a theoretical basis for the study of harmful cotton blind stinkbug. The proposed classification method of cotton blind stinkbug hazard rating will not only promote technical level for the prevention and treatment of the cotton blind stinkbug, but also it provides a reference for the identification and control of other pests.