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Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features

文献类型： 外文期刊

作者： Lin, Fenfang 1 ; Zhang, Dongyan 2 ; Huang, Yanbo 3 ; Wang, Xiu 4 ; Chen, Xinfu 5 ;

作者机构： 1.Nanjing Univ Informat Sci & Technol, Sch Geog & Remote Sensing, Nanjing 210044, Jiangsu, Peoples R China

2.Anhui Univ, Anhui Engn Lab Agroecol Big Data, Hefei 230601, Anhui, Peoples R China

3.USDA ARS, Crop Prod Syst Res Unit, Stoneville, MS 38776 USA

4.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

5.Fujian Vocat Coll Agr, Fuzhou 350007, Fujian, Peoples R China

关键词： hyper spectral imaging;object-oriented;decision tree;corn;weed

期刊名称：SUSTAINABILITY （ 影响因子：3.251； 五年影响因子：3.473 ）

ISSN： 2071-1050

年卷期： 2017 年 9 卷 8 期

页码：

收录情况： SCI

摘要： Accurate detection of weeds in farmland can help reduce pesticide use and protect the agricultural environment. To develop intelligent equipment for weed detection, this study used an imaging spectrometer system, which supports micro-scale plant feature analysis by acquiring high-resolution hyper spectral images of corn and a number of weed species in the laboratory. For the analysis, the object-oriented classification system with segmentation and decision tree algorithms was utilized on the hyper spectral images to extract shape and texture features of eight species of plant leaves, and then, the spectral identification characteristics of different species were determined through sensitive waveband selection and using vegetation indices calculated from the sensitive band data of the images. On the basis of the comparison and analysis of the combined characteristics of spectra, shape, and texture, it was determined that the spectral characteristics of the ratio vegetation index of R677/R710 and the normalized difference vegetation index, shape features of shape index, area, and length, as well as the texture feature of the entropy index could be used to build a discrimination model for corn and weed species. Results of the model evaluation showed that the Global Accuracy and the Kappa coefficient of the model were both over 95%. In addition, spectral and shape features can be regarded as the preferred characteristics to develop a device of weed identification from the view of accessibility to crop/weeds discriminant features, according to different roles of various features in classifying plants. Therefore, the results of this study provide valuable information for the portable device development of intelligent weed detection.

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