Identification and Classification of Bulk Fruits Images using Artificial Neural Networks

The decision-making capabilities of human-inspectors are being affected by external influences such as fatigue, vengeance, bias etc. Hence, development of a Machine Vision Systems (MVS) becomes essential as an alternative to this manual practice in the context of current technological era so as to overcome aforesaid influences. Machine Vision Systems are successfully used for recognition of greenhouse cucumber fruit using computer vision (Libin Zhang et.al, 2007). A method for the classification and gradation of different grains (for a single grain kernel) such as groundnut, Bengal gram, Wheat etc, is described in (B.S Anami et al. 2003). The effect of foreign bodies on recognition and classification of food grains is given in (B.S Anami et al. 2009). Some researchers have used an artificial neural network approach to the color grading of apples (Kazuhiro Nakano,1997) A novel method for segmentation of apple fruit from Video via Background Modeling is carried out by (Amy L. Tabb, et.al 2006). A Robust algorithm for segmentation of food images from a background is presented by (Domingo Mery and Franco Pedreschi, 2005). A high spatial resolution hyper spectral imaging system is presented as a tool for selecting better multispectral methods to detect defective and contaminated food and agricultural products by (Patrick M. Mehl et.al.2004). Some researchers have developed a machine vision system for automatic grading of Mushrooms (P. H Heinemann, et.al, 1994). Some have used an artificial neural network approach to identify and classify the bulk grain samples (McCollum et.al, 2004, B.S Anami et al. 2005, 2006; Kivanc Kilic et al.2006). The present work pertains to the identification and classification of different fruit images. Different samples fruits samples like Apple, Chickoo, Mango, and Orange and Sweet lemon are considered in the work. The fruit images are pre-processed to highlight the discriminating features of the fruit varieties. Thus, the feature vector is obtained and is subjected to categorization process. An artificial neural network based categorizer is developed which is trained using feed forward rule. In this work, we have considered more popular five fruit varieties, their preprocessing, and color and texture feature extraction, neural network model development for fruit identification and classification and finally testing of the proposed methodology against a large number of fruit image samples. The present paper is organized into five sections. Section 2.0 gives the proposed methodology. Section 3.0 describes identification and classification of fruits using a neural network. The results and discussions are given in section 4.0. Section 5.0 gives conclusion of the work.