Inspection and Grading of Dried Coconut – an Automatic Method Using SVM Classification

Rekha Lakshmanan; Nirmal Dorothy; Abid Rahman; Ajmal

doi:10.54060/jieee.2023.102

Authors

Rekha Lakshmanan Computer Science and Engineering, KMEA Engineering College, Ernakulam, India
Nirmal Dorothy Computer Science and Engineering, KMEA Engineering College, Ernakulam, India
Abid Rahman Computer Science and Engineering, KMEA Engineering College, Ernakulam, India
Ajmal Computer Science and Engineering, KMEA Engineering College, Ernakulam, India

DOI:

https://doi.org/10.54060/jieee.2023.102

Keywords:

Copra, Grading, Image Processing, Segmentation, Classification

Abstract

Coconut production and consumption rate of India being very high, contributes significantly towards the Indian economy; thus, ensuring the quality of allied products proves to be a necessity. Intelligent quality evaluation and grading system for copra images is proposed to classify copra, the dried coconut kernel into various categories using an image processing approach. The proposed method is useful in the current scenario, as quality deterioration during drying and storing of copra is a challenging task. Inferior quality of copra due to the presence of moisture, mold, fungi, bacteria and sulphur may adversely affect the shelf life thus deteriorating the quality of final products and human health. The grading system automatically segments the region of interest of copra images using SUSAN filter and classifies them into usable and unusable categories. The unusable categories of copra considered in the proposed method include copra with moisture, mold, wrinkles and sulphur. Various features of copra images were collected and analyzed. The selected features of copra images were used for training and classification using SVM classifier. The proposed method has been evaluated using a real database and the results are promising.

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