Title :
iPepper: Intelligent pepper grading and quality assurance system
Author :
Iskandar, D. N F Awang ; Baini, Rubiyah ; Wee, Alvin Yeo ; Rahman, Shapiee Abdul ; Fauzi, Ahmad Hadinata
Author_Institution :
Fac. of Comput. Sci. & Inf. Technol., Univ. Malaysia Sarawak, Kota Samarahan, Malaysia
Abstract :
Pepper is a key export of the state of Sarawak (Malaysian Borneo); it produces 98% of Malaysia´s pepper. At present, processed pepper berries are graded manually. This process is time consuming and error prone as it is very much dependent on the experience of the pepper grader. To overcome these weaknesses, we propose a Pepper Grading System which employs image processing and machine learning approaches using image features and moisture content data of the pepper berries. For instance, from initial tests, a high correlation between the grade of pepper berries to the colour features has been detected. Using existing machine learning algorithms in WEKA, we have obtained a 100% accuracy in categorising the pepper berries into the correct grades. In addition, moisture content and colourometer readings provide another 2 other parameters which may complement the image features in accurately classifying the berries into the right grades.
Keywords :
agricultural products; food products; image processing; learning (artificial intelligence); production engineering computing; quality control; Malaysian Borneo; Sarawak; colour features; colourometer readings; iPepper; image features; image processing; intelligent pepper grading; machine learning algorithm; moisture content data; pepper berries; quality assurance system; Artificial neural networks; Feature extraction; Image color analysis; Machine learning algorithms; Moisture; Testing; agricultural sciences; computer vision; image processing and analysis;
Conference_Titel :
Signal Processing and its Applications (CSPA), 2011 IEEE 7th International Colloquium on
Conference_Location :
Penang
Print_ISBN :
978-1-61284-414-5
DOI :
10.1109/CSPA.2011.5759919