Abstract
In this paper, we introduce an algorithm for the fitting of bounding rectangle to a closed region of cashew kernel in a given image. We propose an algorithm to automatically compute the coordinates of the vertices closed form solution. Which is based on coordinate geometry and uses the boundary points of regions. The algorithm also computes directions of major and minor axis using least-square approach to compute the orientation of the given cashew kernel. More promising results were obtained by extracting shape features of a cashew kernel, it is proved that these features may predominantly use to make the better distinction of cashew kernels of different grades. The intelligent model was designed using Artificial Neural Network (ANN). The model was trained and tested using Back-Propagation learning algorithm and obtained classification accuracy of 89.74%.Â
Highlights
Cashew tree (Anacardium Occidentale) is a native of southern America and was brought to India by the Portuguese
The network recognizes and classifies the given kernel as a pattern of vector P as belonging to class Oi if the ith output of the network is “high” whereas all other outputs are “low.” A feed-forward neural network was trained for classification of the cashew kernel samples into W-180, W-210, W-240 and W-320 grades
Inputs to the network were shape features computed, and four outputs formed a three–bit binary number representing the category of classification (000 to black, 001 to WW-180, to WW-210, to WW-240, 100 to WW-320 and 111 to white)
Summary
Cashew tree (Anacardium Occidentale) is a native of southern America and was brought to India by the Portuguese. The grading of cashew nuts is based on manual inspection of physically perceived quality attributes such as color, shape, and size. Most of the customer are paying primary importance to quality aspects of food items; their purchase decisions depend critically on items geometric features like size, shape, color, etc., [7]. Keeping these aspects mind, we are trying to build an intelligent model, which can able to understand and analyze the significance of these morphological features (i.e., Shape), and do the classification.
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