Rice Seed Germination Analysis

This research aimed to develop the computer software called “Rice Seed Germination Analysis (RiSGA)” which could predict rice seed image for rice germination by using an image processing technique. The RiSGA consisted of five main process modules: 1) image acquisition, 2) image pre-processing, 3) feature extraction, 4) quality control analysis and 5) quality results. Six variations of Thai rice seed species (CP111, RD41, Chiang Phattalung, Sang Yod Phattalung, Phitsanulok 2 and Chai Nat 1) were used for the experiment. The RiSGA extracted three main features: 1) color, 2) morphological and 3) texture feature. The RiSGA applied four well-known techniques: 1) Euclidean Distance (ED), 2) Rule Based System (RBS), 3) Fuzzy Logic (FL) and 4) Artificial Neural Network (ANN). The RiSGA precision of ED, RBS, FL, and ANN was 87.50%, 100%, 100%, and 100%, respectively. The average access time was 4.35 seconds per image, 5.29 seconds per image, 7.04 seconds per image, and 159.65 seconds per image, respectively.

[1]  B. K. Yadav,et al.  Monitoring milling quality of rice by image analysis , 2001 .

[2]  Luigi Troiano,et al.  Texture recognition by using GLCM and various aggregation functions , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[3]  Giyoung Kim,et al.  Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L.) Seeds Using LED-Induced Hyperspectral Reflectance Imaging , 2014, Sensors.

[4]  W. Chiracharit,et al.  Non-destructive Identification of unmilled rice using digital image analysis , 2012, 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[5]  Patrick Plainchault,et al.  An image acquisition system for automated monitoring of the germination rate of sunflower seeds , 2004 .

[6]  C. B. Silva,et al.  Automated system of seedling image analysis (SVIS) and electrical conductivity to assess sun hemp seed vigor , 2012 .

[7]  Cheng Fang,et al.  Machine Vision Analysis of Characteristics and Image Information Base Construction for Hybrid Rice Seed , 2005 .

[8]  Harpreet Kaur,et al.  Classification and Grading Rice Using Multi-Class SVM , 2013 .

[9]  Antonio Dell'Aquila.  Digital imaging information technology applied to seed germination testing. A review , 2009 .

[10]  K. Fujimura,et al.  An automated system for vigor testing three-day-old soybean seedlings , 2003 .

[11]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[12]  V. Sandeep Varma,et al.  Seed image analysis: its applications in seed science research , 2013 .

[13]  Yande Liu,et al.  An automatic method for identifying different variety of rice seeds using machine vision technology , 2010, 2010 Sixth International Conference on Natural Computation.

[14]  Naimah Mohd Hussin,et al.  Bipartite graph edge coloring approach to course timetabling , 2010, 2010 International Conference on Information Retrieval & Knowledge Management (CAMP).

[15]  G. L. Prajapati,et al.  An efficient colouring of graphs using less number of colours , 2012, 2012 World Congress on Information and Communication Technologies.

[16]  Kavindra R. Jain,et al.  Parametric quality analysis of Indian Ponia Oryza Sativa ssp Indica (rice) , 2013 .

[17]  Mirolyub Mladenov,et al.  Application of neural networks for seed germination assessment , 2008 .

[18]  Antonio Dell’Aquila,et al.  New perspectives for seed germination testing through digital imaging technology. , 2010 .

[19]  Zhiwei Zhu,et al.  Inspection of Rice Appearance Quality Using Machine Vision , 2009, 2009 WRI Global Congress on Intelligent Systems.

[20]  T. Nagatsuka,et al.  Classification of Philippine rice grains using machine vision and artificial neural networks. , 2008 .

[21]  O. Justice.  5 – ESSENTIALS OF SEED TESTING , 1972 .

[22]  F. Cheng,et al.  Identification of rice seed varieties using neural network. , 2005, Journal of Zhejiang University. Science. B.

[23]  Sanjivani Shantaiya,et al.  Identification Of Food Grains And Its Quality Using Pattern Classification , 2010 .

[24]  A. Dell'Aquila,et al.  Application of a computer-aided image analysis system to evaluate seed germination under different environmental conditions , 2004 .

[25]  M. Scott Howarth,et al.  Measurement of seedling growth rate by machine vision , 1993, Other Conferences.

[26]  C. S. Silva,et al.  Classification of Rice Grains Using Neural Networks , 2013 .

[27]  Prajakta Pradip Belsare,et al.  Application Of Image Processing For Seed Quality Assessment: A Survey , 2013 .