|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
|Image segmentation method for cotton leaf under complex background and weather conditions
likai1, zhang jian hua2, feng quan1, kong fan tao2, han shu qing2, wu jian zhai2
1.Engineering College, Gansu Agricultural University;2.Institute of Agricultural Information, Chinese Academy of Agricultural Sciences
|In order to realize the accurate segmentation of cotton leaf under natural conditions, a new image segmentation algorithm was put forward based on particle swarm optimization (PSO) algorithm and K-means clustering algorithm. Firstly, we denoised the cotton leaf images by the two-dimensional convolution filter in the RGB color space model; Secondly, we converted the processed color image from RGB to the Q component , the super G component and the a* component, which had the largest difference between the target and the background in 3 color components; Again, in one dimension data space of K means clustering, we searched for subspace of solutions to the global pixel by using the PSO algorithm, through the iterative search, we could receive global optimal solution and make sure optimal clustering center, so that we could improve the convergence effect of K means clustering; Finally, we divided the pixels, which come from cotton leaf images, into clusters to obtain the results of cotton leaf segmentation. Considering the effects of different weather conditions and different backgrounds on imaging, we took 1200 pictures of cotton leaves under various imaging conditions, and evaluated the segmentation performance of the proposed algorithm on these images. Experimental results showed that, for the images taken in sunny day, cloudy day and after raining, the accuracies of segmentation of the algorithm reached 92.39%, 93.55% and 88.09% respectively, the whole average segmentation accuracy is 91.34% . Though comparing with the traditional K means algorithm, the whole average segmentation accuracy of the proposed algorithm had improved by 5.41% than the traditional K means algorithm. Segmentation results showed that, the proposed algorithm from this paper can accurately segment the cotton leaf images, which combined with 3 weather(sunny day, cloudy day and rainy day) and 4 complex background features(white ground membrane, black film, straw, soil). The algorithm in this paper can provide support for the subsequent processing of feature extraction and identification of plant diseases and insect pests.
|Key words: cotton leaves complex background weather condition K means clustering particle swarm optimization image segmentation