Selection of relevant features for classification from a high dimensional data set by keeping their class discriminatory information intact is a classical problem in Machine Learning. The classification power of the features can be measured from the point of view of redundant information and correlations among them. Choosing minimal set of features optimizes time, space complexity related cost and simplifies the classifier design, resulting in better classification accuracy. In this paper, tomato (Solanum Lycopersicum L) leaves and fruiting habits were chosen with a futuristic goal to build a prototype model of leaf & fruit classification. By applying digital image processing techniques, tomato leaf and fruit images were pre-processed and morphological shape based features were computed. Next, supervised filter and wrapper based feature selection techniques were adopted to choose the optimal feature set leading to small within-class variance and large among-class distance which may be of utter importance in building the model for recognition system of the tomato leaf and fruiting habit genre.

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Cluster analysis is a prime Pattern Recognition method used to categorize sample patterns in a population by means of forming different clusters by assigning cluster memberships to the sample patterns depending on the feature similarity relati onship among different patterns. Patterns displaying dissimilar feature values are assigned different cluster memberships whereas patterns carrying similar feature values are placed into same cluster. Searching the relationship among horticultural data has become a major research area in Pattern Recognition. In this paper we have used the morphological features for describing the characteristics of Tomato leaves and fruits belonging to different classes. Morphological feature values are extracted from different tomato lea f and fruiting habit samples to analyze through K-Means and Two-step clustering techniques to segment leaf and fruit samples into separate clusters according to their species owing to categorize them. Our experimentation also compares and discusses about the importance of the features which are obtained through K-Means and Two-step Clustering technique, may be useful for leaf and fruit species categorization.

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