AQUiD: Automated Quality Assessment Using Digital Image Processing

Abstract

Products in the market are expected to satisfy the consumer’s quality requirements. Agriculture being one of the main occupation of the people of India, the raw products must be sorted to determine whether they fit the quality description so that high quality products are obtained as the end result. The proposed method is designed to ensure the availability of good quality coconut oil in the market by assessing the quality of each individual sample going into the production line. 70% of moisture content present naturally in copra(dried coconut kernel) is dried to almost 7% for coconut oil production. To prevent the growth of bacteria and fungus on the surface of the copra, sulphur is added as a preservative. Allergenic reactions and lung performance restrictions can be caused due to the presence of sulphur in copra. The presence of moisture may also adversely affect oil quality. The texture features such as wrinkles, moulds, fungi growth on the surface also deplete the oil quality. The features of different kinds of copra are analysed and is used train the machine. The machine learning methodology is adopted for the classification of copra as usable and unusable.