Seed Technological Development - A Survey

This paper provides a review of automating or semi-automating the seed quality purity test. Computer vision (CV) technology used in variety of industries is a sophisticated type of inspection technology; however, it is not widely used in agriculture.The application of CV technologies is very challenging in agriculture. As CV plays an important role in this domain, research in this area has been motivated. Several theories of automating seed quality purity test are briefly mentioned. The reviewed approaches are classified according to features and classifiers. The methods for extracting features of a particular seed, and the classifiers used for classifying the seeds, are mentioned in the paper. An overview of the most representative methods for feature extraction and classification of seeds is presented. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in automation of seed classification, regardless of particular feature or classifier.

[1]  Pablo M. Granitto,et al.  Large-scale investigation of weed seed identification by machine vision , 2005 .

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

[3]  Hamid Reza Pourreza,et al.  Identification of nine Iranian wheat seed varieties by textural analysis with image processing , 2012 .

[4]  Xing Li,et al.  Combining genetic algorithm and SVM for corn variety identification , 2011, 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC).

[5]  Kuo-Yi Huang,et al.  Detection and classification of areca nuts with machine vision , 2012, Comput. Math. Appl..

[6]  Wasin Sinthupinyo,et al.  Color and texture for corn seed classification by machine vision , 2011, 2011 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS).

[7]  Marian Wiwart,et al.  Identification of hybrids of spelt and wheat and their parental forms using shape and color descriptors , 2012 .

[8]  T. F. Burks,et al.  Evaluation of Neural-network Classifiers for Weed Species Discrimination , 2005 .

[9]  Syed Abdul Mutalib Al Junid,et al.  An Intelligent Classification Model for Rubber Seed Clones Based on Shape Features through Imaging Techniques , 2010, 2010 International Conference on Intelligent Systems, Modelling and Simulation.

[10]  Pablo M. Granitto,et al.  Weed seeds identification by machine vision , 2002 .

[11]  Kamal Hammouche,et al.  Automatic seeds recognition by size, form and texture features , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[12]  Wei Li,et al.  Combining discriminant analysis and neural networks for corn variety identification , 2010 .

[13]  Karpagavalli S.,et al.  Classification of Seed Cotton Yield Based on the Growth Stages of Cotton Crop Using Machine Learning Techniques , 2010, 2010 International Conference on Advances in Computer Engineering.