Investigations into a Drunken Flower Pollination Algorithm for Auto-Grading of Edible Birds Nest

Lee, Wei Wen (2021) Investigations into a Drunken Flower Pollination Algorithm for Auto-Grading of Edible Birds Nest. Final Year Project (Bachelor), Tunku Abdul Rahman University College.

Text Lee Wei Wen.pdf Restricted to Registered users only Download (2MB)

Abstract

Edible Bird Nest (EBN) produced by certain species of swiftlets has been known of its source of protein and vitamins that benefit the human body. This results in high demand from humanity due to the advantages of consuming the EBN. However, manual process of grading and classifying the EBN for different price range may cause drawbacks towards the production of EBN. The grading of EBN is done by observing the color, shape, size and impurities present in the nest. Although manual process is done by trained personnel, the results obtained are often inconsistent and inaccurate due to human fatigue. Hence, this process is tedious and time consuming which may cause delay in the production of EBN. To overcome this issue, an algorithm featuring the novelty of Drunken Flower Pollination Algorithm (DFPA) is developed in this paper to perform classification on the EBN automatically. An average accuracy of nearly 88% is achieved using the proposed method as the classifier.