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Journal of Integrative Agriculture  2021, Vol. 20 Issue (11): 2880-2891    DOI: 10.1016/S2095-3119(20)63556-0
Special Issue: Triticeae Crops Physiology · Biochemistry · Cultivation · Tillage
Crop Science Advanced Online Publication | Current Issue | Archive | Adv Search |
Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data
LIU Da-zhong, YANG Fei-fei, LIU Sheng-ping
Key Laboratory of Agricultural Information Service Technology, Ministry of Agriculture and Rural Affairs/Intelligent Agriculture Research Office, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
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Abstract  
Fractional vegetation cover (FVC) is an important parameter to measure crop growth.  In studies of crop growth monitoring, it is very important to extract FVC quickly and accurately.  As the most widely used FVC extraction method, the photographic method has the advantages of simple operation and high extraction accuracy.  However, when soil moisture and acquisition times vary, the extraction results are less accurate.  To accommodate various conditions of FVC extraction, this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index (NDVI) greyscale image of wheat by using a density peak k-means (DPK-means) algorithm.  In this study, Yangfumai 4 (YF4) planted in pots and Yangmai 16 (Y16) planted in the field were used as the research materials.  With a hyperspectral imaging camera mounted on a tripod, ground hyperspectral images of winter wheat under different soil conditions (dry and wet) were collected at 1 m above the potted wheat canopy.  Unmanned aerial vehicle (UAV) hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.  The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat, and the extraction effects of the two methods were compared and analysed.  The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered, while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.  The absolute values of error were 0.042 and 0.044, the root mean square errors (RMSE) were 0.028 and 0.030, and the fitting accuracy R2 of the FVC was 0.87 and 0.93, under dry and wet soil conditions and under various time conditions, respectively.  This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction. 
Keywords:  fractional vegetation cover        k-means algorithm        NDVI       vegetation index        wheat   
Received: 24 March 2020   Accepted: 17 September 2021
Fund: This work was supported by the Beijing Natural Science Foundation, China (4202066), the Central Public-interest Scientific Institution Basal Research Fund, China (JBYW-AII-2020-29 and JBYW-AII-2020-31), the Key Research and Development Program of Hebei Province, China (19227407D), and the Technology Innovation Project Fund of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2020-All).
Corresponding Authors:  Correspondence LIU Sheng-ping, E-mail: liushengping@caas.cn   

Cite this article: 

LIU Da-zhong, YANG Fei-fei, LIU Sheng-ping. 2021. Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data. Journal of Integrative Agriculture, 20(11): 2880-2891.

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