农业工程学报Issue(10)：138-144,7.DOI:10.3969/j.issn.1002-6819.2014.10.017
基于改进灰度共生矩阵和粒子群算法的稻飞虱分类
Classification of rice planthopper based on improved gray level co-occurrence matrix and particle swarm algorithm
摘要
Abstract
The rice planthopper images acquired by remote real-time recognition system usually have poor quality, and hence it is impossible to classify rice planthoppers using the color features of rice planthopper images. This study proposed to extract texture features of images based on gray level co-occurrence matrix (GLCM) and used the texture features to classify rice planthoppers. A H-shape mobile photographing device designed by us was used to obtain color images of rice planthoppers. The color images were grayed by formula, and then the background of images was removed using Otsu image segmentation method to generate binary images followed by calculation through the binary image coordinates. The GLCM was improved to extract texture features of images without background. Specifically, the center of gravity was determined by coordinates of the images and considered as the center to construct GLCM. The images of the rice planthopper were copied into the sub images with 160 pixels×160 pixels based on the center. Using multiple annular routes, the features of rice planthopper gray images were extracted including energy, entropy, moment of inertia and correlation. In the training and testing experiment of the extracted features, back propagation (BP) nerve network and optimized BP nerve network based on parametric selection -improved particle swarm optimization algorithm were individually used to train and classify the rice planthopper, and the training time and identification rate of each method were compared. A total of 300Sogatella,Laodelphax andNilaparvata lugens with 100 samples for each type of rice planthopper was trained. The training time using the optimized BP nerve network based on improved particle swarm optimization algorithm was only 0.5683 seconds, which was far less than that (29.5772 seconds) using BP neural network. Based on the BP neural network, the identification rate reached 80% forSogatella, 90% forLaodelphax, and 95% forNilaparvata lugens. Based on the improved particle swarm optimization algorithm-optimized BP nerve network, the identification rate reached 90% forSogatella, 95% forLaodelphax, and 100% forNilaparvata lugens. Therefore, the identification rate of the optimized BP neural network based on parametric selection-improved particle swarm optimization algorithm was higher than that of BP neural network. Furthermore, the shorter training time using the optimized BP neural network based on parametric selection-improved particle swarm optimization algorithm than using the BP neural network suggested that the former could better meet the requirement of real time optimization.关键词
神经网络/图像识别/分类/粒子群/稻飞虱/灰度共生矩阵Key words
neural networks/image recognition/classification/particle swarm optimization/rice planthopper/gray level co-occurrence matrix分类
信息技术与安全科学引用本文复制引用
邹修国,丁为民,陈彩蓉,刘德营..基于改进灰度共生矩阵和粒子群算法的稻飞虱分类[J].农业工程学报,2014,(10):138-144,7.基金项目
国家高技术研究发展计划（863计划）资助项目（2012AA101904）；公益性行业（农业）科研专项资助项目（201203059）；南京农业大学青年科技基金资助项目 （）
