Abstract

Cashew is a commercial commodity that plays a major role in earning foreign revenue among export commodities in India. The purpose of this research work is to explore image processing techniques and approaches on Indian cashew variety identification based on their kernels. Colour is an important quality factor for grading, marketing, and end users. Our primary objective is to develop a cost-effective intelligent model to identify the cashew kernels. Colour features in the RGB (red-green-blue) colour space are extracted and computed. A feed-forward neural network is trained to classify sample cashew kernels. An intelligent classification system based on computer vision system can be developed for automated grading and sorting to speed up the classification of cashew kernels. This will solve the major problems of many of the cashew export industries also, gives justice to the cashew growing farmers in accurate grading. The classification system is evaluated on cashew kernels of 6 different grades. The result of our study shows that, the system gives about 80% classification rate.

Highlights

  • Cashew (Anacardium occidentale L.) a native of Eastern Brazil was introduced to India by the Portuguese nearly five centuries back

  • The algorithm was developed for texture feature extraction using Gray level Co-occurrence Matrix (GLCM) method performed well in the task of extracting texture features from images of different cashew kernels white wholes

  • The results obtained in this work indicate that the ANNs can in general classify cashew kernels white wholes with success rate of 80% to 90%

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Summary

Introduction

Cashew (Anacardium occidentale L.) a native of Eastern Brazil was introduced to India by the Portuguese nearly five centuries back. Grades Based on the Size, Shape, and Colour of the cashew kernels are graded into white or scorched wholes, pieces, splits, butts etc. Grading of cashew kernels is based on inspection of physical quality attributes such as colour, shape, and size. By using these physical attributes, a trained person determines the cashew kernel of which class (i.e., white wholes). An objective and cost–effective computer vision system is needed to segregate cashew kernels Such a system would facilitate cashew grading and serve as a quality control tool for Cashew kernels classification using texture features processing facilities such as elevators, seed cleaning plants, and oil mills[1][2]. The present work pertains to classification of cashew kernel white whole based on textural features

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