Affiliations: [a] Department of Electronics and Communication Engineering, Future Institute of Engineering and Management, Kolkata, India | [b] Department of Printing Engineering, Jadavpur University, Kolkata, India | [c] Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata, India
Correspondence:
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Corresponding author: Arpitam Chatterjee, Department of Printing Engineering, Jadavpur University, Salt Lake Campus, Block – LB, Plot – 8, Sector – 3, Kolkata, PIN – 700106, India. E-mails: [email protected], arpitam.chatterjee@ jadavpuruniversity.in. ORCID: 0000-0003-3850-694X.
Abstract: Adulteration in different spices is an emerging challenge in human civilization. It is commonly detected using different analytical and instrumental techniques. Despite good accuracy and precision many of such techniques are limited by their high processing time, skilled manpower requirement, expensive machinery and portability factor. Computer vision methodology driven by powerful convolutional neural network (CNN) architectures can be a possible way to address those limitations. This paper presents a CNN driven computer vision model which can detect cornstarch adulteration in turmeric powder along with the degree of adulteration. The model has been optimized using binary genetic algorithm (BGA) for improved performance and consistency. The experimentations presented in this paper were conducted with an in-house database prepared for 4 levels of adulteration and found to provide about 98% overall accuracy. The less expensive and faster detection capability of the model along with its mobility makes this proposal a promising addition to the existing spice adulteration screening methods.
