Weed Identification Based on Shape Features and Ant Colony Optimization Algorithm
In order to improve the accuracy and efficiency of weed recognition, an identification method based on ant colony optimization (ACO) algorithm and support vector machine (SVM) is proposed. Firstly, shape feature parameters are extracted from the plant leaves after a series of image processing such as threshold segmentation, smooth processing and edge detection etc., and five geometric parameters and seven Hu-moment invariants which have useful properties is utilized to produce feature vectors. Then ACO algorithm in combination with SVM classifier is used to select the optimal feature set for classification. Finally, proposed approach has been applied on lab plant image database of cotton field and the experimental results have shown that the method can optimize feature subset and achieve an identification rate over 94％ which is higher than using the original feature set.
image processing shape features feature selection ACO algorithm weed identification
Xianfeng Li Zhong Chen
School of Information Engineering Yancheng Institute of Technology Yancheng, P.R.China School of Electrical Engineering Yancheng Institute of Technology Yancheng, P.R.China
国际会议
太原
英文
384-387
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）