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ARS Home » Southeast Area » Stoneville, Mississippi » Crop Production Systems Research » Research » Publications at this Location » Publication #343985
Title: Detection of corn and weed species by the combination of spectral, shape and textural features

Author
item LIN, FENFANG - Nanjing Tech University
item ZHANG, DONGYAN - Anhui Agricultural University
item Huang, Yanbo
item WANG, XIU - National Engineering Research Center For Information Technology In Agriculture
item CHEN, XINFU - National Engineering Research Center For Information Technology In Agriculture

Submitted to: Sustainability
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/20/2017
Publication Date: 8/25/2017
Citation: Lin, F., Zhang, D., Huang, Y., Wang, X., Chen, X. 2017. Detection of corn and weed species by the combination of spectral, shape and textural features. Sustainability. 9(8):1-14.

Interpretive Summary: It is important to accurately detect weeds in crop fields to help reduce pesticide use and protect the environment. In order to develop intelligent scheme for weed detection, scientists in Nanjing University of Information Science and Technology, Anhui University, USDA-ARS Crop Production Systems Research Unit at Stoneville, Mississippi, and National Engineering Research Center for Information Technology in Agriculture, China collaboratively developed a new method for small-scale plant feature analysis of high-resolution hyperspectral images of corn and weeds. The results show high accuracies in differentiating corn from weeds in the crop field. This study provides valuable information for developing portable device used for rapid detection of weeds in the crop field.

Technical Abstract: 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.