Weed seeds recognition using Locally Linear Embedding

In this paper, a novel technique for the recognition of weed seeds, known as Locally Linear Embedding (LLE), is presented. This experiment contains two steps: dimensionality reduction with LLE and classification. In dimensionality reduction part, LLE, which is an unsupervised non-linear technique, is used to map high dimensional data into low dimensional, neighborhood-preserving embeddings and extract features. In classification part, minimum Euclidean distance classifier is used to recognize different weed seeds. This paper presents a comparison between LLE and PCA. Experimental results demonstrate that the representation of LLE is superior significantly as expected and gains much higher recognition rates, amounting to 96%, which is 8% higher than those of PCA.