A Supervised Learning Approach to Detect Black Pepper Adulteration
No Thumbnail Available
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Uva Wellassa University of Sri Lanka
Abstract
Black pepper is one of the widely planted spices in the world and its cost is very high when compared
to other spices. Many People earn huge income from black pepper by exporting and selling.
Therefore, they mix unwanted adulterants, such as stones, weeds and other low-cost items in order to
increase the quantity and the profit. Out of these adulterants, papaya seeds are very common, as their
appearance is very similar to black pepper seeds. Those malpractices will reduce the quality of
pepper samples and it is difficult to control as these two types of seeds cannot be easily classified
even by an expert eye due to the smaller size in bulk samples. Hence, more advanced solutions are
required to determine the adulteration of pepper samples. Currently, there are some existing studies
and methods; one is a manual method to separate papaya and pepper seeds using water by
considering their weights. Further, there are chemical methods, such as the Thin-Layer
Chromatography (TLC) approach to detect the adulterated papaya seeds by using mixed samples of
black paper powder and ground papaya seed. In this study, a classification method was proposed to
differentiate black pepper from papaya seeds using the Convolutional Neural Network (CNN)
technique. The images of the samples were captured using a high-resolution digital camera and the
features, such as size and shape were extracted to classify the seeds. Next, these features were fed as
the input to the CNN model for the classification task. The experimented model was able to
successfully label the papaya and black pepper seeds with an accuracy rate of 85.94%. As the main
output of the model, the percentages of papaya and pepper seeds in the given sample were presented.
To improve the accuracy of the model, high-quality images and more features such as texture and
color will be used in future work.
Keywords: Black Pepper Adulteration; Feature Extraction; Image Processing; Supervised Learning
Description
Keywords
Agriculture, Crop Production, Spice, Black Pepper, Computing and Information Science