An image-based approach for automatic detecting tasseling stage of maize using spatio-temporal saliency

In this paper, we explored the application of computer vision technology to automatically detect the tasseling stage of maize. The commonly used HOG/SVM detection framework is chosen to recognize the ears of maize for determining the occurrence time of the stage. However, it cannot guarantee high precision rate. Thus, we proposed a new method called Spatio-temporal Saliency Mapping to highlight the ear while suppress the background, which significantly improve the detection performance. Comparing experiment has been carried out to testify the validity of our method and the results indicate that our method can meet the demand for practical observation.

[1]  Mohammad Bannayan,et al.  Weather conditions associated with irrigated crops in an arid and semi arid environment , 2011 .

[2]  F. Cointault,et al.  In‐field Triticum aestivum ear counting using colour‐texture image analysis , 2008 .

[3]  Wei Guo,et al.  Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model , 2013 .

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Sridha Sridharan,et al.  Spatio Temporal Feature Evaluation for Action Recognition , 2012, 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA).

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Cristian Flueraru,et al.  A Validation of MODIS Snowcover Products in Romania: Challenges and Future Directions , 2007, Trans. GIS.

[8]  Zhenghong Yu,et al.  Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage , 2013 .

[9]  Gilles Rabatel,et al.  Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat , 2011 .

[10]  Chenghai Yang,et al.  Comparison of QuickBird Satellite Imagery and Airborne Imagery for Mapping Grain Sorghum Yield Patterns , 2006, Precision Agriculture.

[11]  A. Kethsy Prabhavathy,et al.  USING BOOSTING HOG FEATURES FOR VEHICLE DETECTION IN LOW-ALTITUDE AIRBORNE VIDEOS , 2012 .

[12]  Zhenghong Yu,et al.  Automatic measurement of crops canopy height based on monocular vision , 2011, International Symposium on Multispectral Image Processing and Pattern Recognition.

[13]  Jorge Stolfi,et al.  T-HOG: An effective gradient-based descriptor for single line text regions , 2013, Pattern Recognit..

[14]  Jiwang Zhang,et al.  Root growth, available soil water, and water-use efficiency of winter wheat under different irrigation regimes applied at different growth stages in North China , 2010 .

[15]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[16]  Zhengyou Zhang,et al.  A Survey of Recent Advances in Face Detection , 2010 .

[17]  J. Hanway.  How a corn plant develops , 1966 .

[18]  Andrew E. Suyker,et al.  An alternative method using digital cameras for continuous monitoring of crop status , 2012 .