Abstract
Greenness identification from crop growth monitoring images is the first and important step for crop growth status analysis. There are many methods to recognize the green crops from the images, and the visible spectral-index based methods are the most commonly used ones. But these methods can not work properly when dealing with images captured outdoors due to the high variability of illumination and the complex background elements. In this paper, a new approach for greenness identification from maize images is proposed. Firstly, the crop image was converted from RGB color space to HSV color space to obtain the hue and saturation value of each pixel in the image. Secondly, most of the background pixels were removed according to the hue value range of greenness. Then, the green crops were identified from the processed image using the excess green index method and the Otsu method. Finally, all noise objects were removed to get the real crops. The experimental results indicate that the proposed approach can recognized the green plants correctly from the maize images captured outdoors.
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Acknowledgements
The authors thank The Ministry of Science and Technology of the People’s Republic of China (2013DFA11320), The Natural Science Foundation of Hebei Provence (F2015201033), for their financial support.
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Yang, W., Zhao, X., Wang, S., Chen, L., Chen, X., Lu, S. (2015). A New Approach for Greenness Identification from Maize Images. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_33
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DOI: https://doi.org/10.1007/978-3-319-22180-9_33
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