中国农业大学学报2018，Vol.23Issue(2)：88-98,11.DOI:10.11841/j.issn.1007-4333.2018.02.12
复杂背景与天气条件下的棉花叶片图像分割方法
Image segmentation method for cotton leaf under complex background and weather conditions
摘要
Abstract
In order to realize the accurate segmentation of cotton leaf under natural conditions,a new image segmentation method based on particle swarm optimization (PSO) algorithm and K-means clustering algorithm was proposed.A two-dimensional convolution filter was used to denoise cotton leaf images in RGB color space,and the denoised cotton images were converted from RGB components into Q,super G and a* components with larger difference between cotton and the background.The subspace of solutions to the global pixel was calculated in one dimension data space of K means clustering by using the PSO algorithm later.The global optimal solution was acquired by iterative search.The optimal clustering center was determined.The convergence effect of K means clustering was improved by this method.Finally,pixels were classified into different clusters and the leaf area was segmented.Considering the effects of different weather conditions and different backgrounds on imaging,1 200 images of cotton leaves under various imaging conditions were captured.The segmentation performance of the proposed algorithm was investigated based on these images.The experimental results showed that the cotton image segmentation accuracies of the algorithm under sunny,cloudy and raining conditions were 92.39％,93.55％ and 88.09％,respectively.The overall average segmentation accuracy was 91.34％.Compared with the traditional K means algorithm,the overall average segmentation accuracy of the proposed algorithm was improved by 5.41％.The results showed that the proposed algorithm accurately segmented the cotton leaf images,which combined with 3 weather conditions (sunny day,cloudy day and rainy day) and 4 complex background features (white mulch film,black mulch film,straw,soil).The proposed algorithm could be applied to feature extraction and plant diseases and pests identification.关键词
棉花叶片/复杂背景/天气条件/K均值聚类/粒子群优化(PSO)/图像分割Key words
cotton leaf/complex background/weather condition/K means clustering/particle swarm optimization/image segmentation分类
信息技术与安全科学引用本文复制引用
李凯,张建华,冯全,孔繁涛,韩书庆,吴建寨..复杂背景与天气条件下的棉花叶片图像分割方法[J].中国农业大学学报,2018,23(2):88-98,11.基金项目
国家自然科学基金项目(31501229) （31501229）
