Main Article Content
Seed analysis and classification can provide additional information in the production of quality seeds. Generally, these activities are performed by specialists through visual inspection of samples of seeds. However, manual visual inspection of samples is a very tedious and time consuming task. Therefore, automation in seed analysis and classification is required. The work presented in this paper focuses on seed classification techniques useful for accurate classification of seeds. Different feature models, namely, color, texture-n-shape, shape-n-color, texture, and various combinations of the color, shape and texture were tested for the desired classification performances. Most of the feature extraction algorithms use the color, shape and texture features for classification. NNs have been used most of the time as they are universal functional approximators, are data driven self-adaptive, are nonlinear models, due to which are supple enough to model actual real world problems.With the help of experimental results, it has been confirmed that the individual features were showing the desired performance of seed classification near to the standards as prescribed by the International Seed Testing Association (ISTA).