Lawn weed recognition based on improved fuzzy C-means clustering algorithm

【Objective】 In order to realize the precise application of herbicides for lawn weed management, an improved fuzzy C-means (FCM) clustering segmentation algorithm was proposed to solve the problem that it is difficult to segment weeds due to the similar color between weeds and lawns in natural environment.【Method】 The region of interest was extracted by extra-green operator, and the multi-channel information in HSV space was incorporated for image preprocessing to expand the feature difference between weeds and lawns. The filtering range was constrained using region area to remove the lawn background noise in the preprocessed image and reduce the gray level loss in the target region caused by median filtering. An anisotropic detection operator of difference of gray distribution(DGD) was proposed. In the clustering process, the gray distribution difference characteristics in different directions around pixels were introduced to realize lawn weed segmentation.【Result】 Compared with the traditional FCM, FCM-S2, FCMNLS and RSFCM algorithms, the algorithm in this paper (DGDFCM) had better suppression effect on most noise areas and could achieve ideal weed segmentation effect. The DGDFCM algorithm could effectively segment lawn weeds, and the average segmentation accuracy was 91.45%, which was 16.35%, 4.12%, 6.80% and 8.06% higher than FCM, FCM-S2, FCMNLS and RSFCM algorithms, respectively.【Conclusion】 The DGDFCM algorithm can effectively segment lawn weeds in natural environment, provides the condition of precise application of herbicides for lawn weeds, and has a practical application value.