Identification of seedling cabbages and weeds using hyperspectral imaging

Authors

  • Deng Wei The National Engineering Research Center for Information Technology in Agriculture
  • Yanbo Huang 2. United States Department of Agriculture, Agricultural Research Service, Crop Production Systems Research Unit, Stoneville, Mississippi, USA;
  • Zhao Chunjiang 1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and forestry Science, Beijing 100097, China; 3. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 4. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; 5. Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
  • Wang Xiu 1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and forestry Science, Beijing 100097, China; 3. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 4. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; 5. Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China

DOI:

https://doi.org/10.25165/ijabe.v8i5.1492

Keywords:

hyperspectral imaging, weed identification, cabbage, seedlings

Abstract

Target detection is one of research focuses for precision chemical application. This study developed a method to identify seedling cabbages and weeds using hyperspectral imaging. In processing the image data with ENVI software, after dimension reduction, noise reduction, de-correlation for high-dimensional data, and selection of the region of interest, the SAM (Spectral Angle Mapping) model was built for automatic identification of cabbages and weeds. With the HSI (Hyper Spectral Imaging) Analyzer, the training pixels were used to calculate the average spectrum as the standard spectrum. The parameters of the SAM model, which had the best classification results with 3-point smoothing, zero-order derivative, and 6-degrees spectral angle, was determined to achieve the accurate identification of the background, weeds, and cabbages. In comparison, the SAM model can completely separate the plants from the soil background but not perfect for weeds to be separated from the cabbages. In conclusion, the SAM classification model with the HSI analyzer could completely distinguish weeds from background and cabbages. Keywords: hyperspectral imaging, weed identification, cabbage, seedlings DOI: 10.3965/j.ijabe.20150805.1492 Citation: Deng W, Huang Y B, Zhao C J, Wang X. Identification of seedling cabbages and weeds using hyperspectral imaging. Int J Agric & Biol Eng, 2015; 8(5): 65－72.

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Published

2015-10-31

How to Cite

Wei, D., Huang, Y., Chunjiang, Z., & Xiu, W. (2015). Identification of seedling cabbages and weeds using hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 8(5), 65–72. https://doi.org/10.25165/ijabe.v8i5.1492

Issue

Vol. 8 No. 5 (2015): IJABE

Section

Information Technology, Sensors and Control Systems

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