Rice and wheat grain counting method and software development based on Android system

We proposed a fast rice and wheat grain counting methods based on mobile phones.A new formula on image feature points and the number of grains was put forward.The counting time of this study is generally less than conventional methods. Thousand grain weight is the main component of rice and wheat yields, and an important indicator for variety breeding and cultivation management. Grain counting is an essential step for thousand grain weight measurement. Among several current counting methods, manual counting is laborious and time-consuming; electronic counting devices are expensive; the counting accuracy based on image segmentation processing is not high; and their uses are inconvenient. This study attempts to develop an application program (APP) for fast rice and wheat grain counting based on Android mobile phones for convenient use. The study identifies the relationship between image feature points and the number of grains, explores the measurement method of image feature points, and compares it with existing counting methods in terms of similarities and differences. The study also formulates grain counting calculations and develops an application program that is easy to operate. The high accuracy of this counting method has been demonstrated by tests of different varieties. The error ratio are below 2%. The program has a short running time. The counting time is generally less than one second (1s) for no more than 400 seeds. The program is convenient and easy to operate. The counting and batch-processing operations are simple. In summary, the grain counting method built in this study can be used as an effective rice and wheat grain counting tool. This study also provides a reference for the development of application programs for grain counting of other kinds of crops.

[1]  LiGuangyao,et al.  A geometric active contour model without re-initialization for color images , 2009 .

[2]  He Dongjian,et al.  Image segmentation algorithm of touching rice kernels based on active contour model. , 2010 .

[3]  Paul C. van Oorschot,et al.  A methodology for empirical analysis of permission-based security models and its application to android , 2010, CCS '10.

[4]  Yong He,et al.  A novel matching algorithm for splitting touching rice kernels based on contour curvature analysis , 2014 .

[5]  N. B. McLAUGHLIN,et al.  DESIGN AND PERFORMANCE OF AN ELECTRONIC SEED COUNTER , 1976 .

[6]  L. Joshua Leon,et al.  Watershed-Based Segmentation and Region Merging , 2000, Comput. Vis. Image Underst..

[7]  H. K. Mebatsion,et al.  A Fourier analysis based algorithm to separate touching kernels in digital images , 2011 .

[8]  Guangyao Li,et al.  A geometric active contour model without re-initialization for color images , 2009, Image Vis. Comput..

[9]  Hongbing Chen,et al.  A Novel Segmentation Algorithm of Fingerprint Image , 2011 .

[10]  W. Wang And J. Paliwal,et al.  Separation and identification of touching kernels and dockage components in digital images , 2006 .

[11]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[12]  Patrick D. McDaniel,et al.  Semantically rich application-centric security in Android , 2012 .

[13]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[14]  Xinkai Zhu,et al.  A shadow-based method to calculate the percentage of filled rice grains , 2016 .

[15]  Digvir S. Jayas,et al.  Digital image analysis for software separation and classification of touching grains. I. Disconnect algorithm , 1995 .

[16]  Abraham Duarte,et al.  Improving image segmentation quality through effective region merging using a hierarchical social metaheuristic , 2006, Pattern Recognit. Lett..

[17]  David E. Reynolds,et al.  Automatic segmentation , 1986 .

[18]  Digvir S. Jayas,et al.  Digital image analysis for software separation and classification of touching grains. II. Classification , 1995 .

[19]  Gustavo A. Slafer,et al.  Coarse and fine regulation of wheat yield components in response to genotype and environment , 2014 .

[20]  Guillermo Ariel García,et al.  Post-anthesis warm nights reduce grain weight in field-grown wheat and barley , 2016 .

[21]  Ping Zhou,et al.  A novel segmentation algorithm for clustered slender-particles , 2009 .

[22]  S. Mccouch,et al.  Fine Mapping of a Grain-Weight Quantitative Trait Locus in the Pericentromeric Region of Rice Chromosome 3 , 2004, Genetics.

[23]  R M Carter,et al.  Rule based concave curvature segmentation for touching rice grains in binary digital images , 2009, 2009 IEEE Instrumentation and Measurement Technology Conference.

[24]  Li Wei,et al.  Automatic segmentation of touching corn kernels in digital image. , 2010 .

[25]  Wasin Sinthupinyo,et al.  Segmentation algoritm for touching round grain image , 2010, 2010 International Conference on Electronics and Information Engineering.

[26]  Yasuomi Ibaraki,et al.  Development of an Android-tablet-based system for analyzing light intensity distribution on a plant canopy surface , 2016, Comput. Electron. Agric..

[27]  Changming Sun,et al.  Splitting touching cells based on concave points and ellipse fitting , 2009, Pattern Recognit..

[28]  Stuart Helliwell,et al.  The effect of growing sites on grain quality of oats and pasting properties of oatmeals , 1999 .

[29]  Y.-C. Wang,et al.  Automatic segmentation of touching rice kernels with an active contour model , 2004 .