Abstract
Abstract: Weed species identification is the premise to control weeds in smart agriculture. It is a challenging topic to control weeds in field, because the weeds in field are quite various and irregular with complex background. An identification method of weed species in crop field is proposed based on Grabcut and local discriminant projections (LWMDP) algorithm. First, Grabcut is used to remove the most background and K-means clustering (KMC) is utilized to segment weeds from the whole image. Then, LWMDP is employed to extract the low-dimensional discriminant features. Finally, the support vector machine (SVM) classifier is adopted to identify weed species. The characteristics of the method are that (1) Grabcut and KMC utilize the texture (color) information and boundary (contrast) information in the image to remove the most of background and obtain the clean weed image, which can reduce the burden of the subsequent feature extraction; (2) LWMDP aims to seek a transformation by the training samples, such that in the low-dimensional feature subspace, the different-class data points are mapped as far as possible while the within-class data points are projected as close as possible, and the matrix inverse computation is ignored in the generalized eigenvalue problem, thus the small sample size (SSS) problem is avoided naturally. The experimental results on the dataset of the weed species images show that the proposed method is effective for weed identification species, and can preliminarily meet the requirements of multi-row spraying of crop based on machine vision.
Highlights
In the process of crop growth, various weeds compete with crop seedlings for nutrients, water, light, and growth space, which severally affect the normal growth of crop seedlings and reduce the yield of crops
Many manifold learning algorithms have been presented for image classification, such as discriminant locality preserving projections (DLPP) (Lu et al, 2010), maximum neighborhood margin discriminant projection (MNMDP) (Gou et al, 2014), weighted neighborhood maximum margin discriminant embedding (WNMMDE) (Jiang et al, 2016), weighted maximum margin discriminant analysis (WMMDA) (Zheng et al, 2005), Local weighted maximum margin discriminant analysis (LWMMDA) (Wang et al, 2007) and supervised global-locality preserving projection (SGLPP) (Shao, 2019)
To validate the performance of the local weighted maximum discriminant projection (LWMDP) based weed recognition method, a lot of experiments are implemented on a weed image dataset, and compared with four state-ofthe-art weed identification algorithms: weed type recognition based on shape descriptors and a fuzzy decision-making method (SDFDM) (Herrera et al, 2014), Classification of crops and weeds from digital images: A support vector machine approach (DISVM) (Ahmed et al, 2012), maize/weed classification by color indices with support vector data description in outdoor fields (CISVDD) (Zheng et al, 2017), and weed identification by SVM based texture feature classification (SVMTFC) (Rojas et al, 2017)
Summary
In the process of crop growth, various weeds compete with crop seedlings for nutrients, water, light, and growth space, which severally affect the normal growth of crop seedlings and reduce the yield of crops. Many manifold learning algorithms have been presented for image classification, such as discriminant locality preserving projections (DLPP) (Lu et al, 2010), maximum neighborhood margin discriminant projection (MNMDP) (Gou et al, 2014), weighted neighborhood maximum margin discriminant embedding (WNMMDE) (Jiang et al, 2016), weighted maximum margin discriminant analysis (WMMDA) (Zheng et al, 2005), Local weighted maximum margin discriminant analysis (LWMMDA) (Wang et al, 2007) and supervised global-locality preserving projection (SGLPP) (Shao, 2019) From these manifold learning methods, it is found that they have two common steps, i.e., construct the local neighborhood structures of sample points on manifolds and map the sample points globally to a low-dimensional space by the local neighborhood structures.
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