Lawn Weed Recognition Based on Fused Feature Fuzzy C-Means Clustering Algorithm

For lawn, weeds are one of the important factors that cause its degradation and endanger agricultural production. In order to achieve accurate application of herbicides for lawn weed management, aiming at the problem that weeds are difficult to be segmented due to the similar color between weeds and lawns in natural environment, this paper proposed an improved fuzzy C-means (FCM) clustering segmentation algorithm. Firstly, the region of interest was extracted by extra-green segmentation and converted to HSV space for multi-channel difference fusion for image preprocessing to expand the feature difference between weeds and lawns. Secondly, the median filtering was carried out under the constraint of area area to remove the lawn background noise in the preprocessed image while maintaining the details of weed leaves. Then, an anisotropic detection operator of difference of gray distribution(DGD) was proposed, which introduces the features of different directions of gray distribution around pixels in the clustering process to achieve lawn weed segmentation. Finally, one versus rest support vector machines(OVR SVMs) were constructed to solve the multi classification problem of lawn weeds. To solve the problem that weeds overlap and occlude each other, which leads to the reduction of local area identification, an optimization method of extracting feature information from image blocks is proposed. The experimental results show that, compared with the traditional FCM algorithm, the algorithm in this paper has a better suppression effect on most noise areas, can effectively segment and identify weeds in the natural environment, which has practical application value.