Open AccessComprehensive Pineapple Segmentation Techniques with Intelligent Convolutional Neural Network
Author(s) -
Mohd Nawawi,
Fatimah Sham Ismail,
Hazlina Selamat
Publication year - 2018
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v10.i3.pp1098-1105
Subject(s) - artificial intelligence , computer science , hue , convolutional neural network , segmentation , minimum bounding box , computer vision , pattern recognition (psychology) , image segmentation , template matching , process (computing) , enhanced data rates for gsm evolution , object detection , image (mathematics) , operating system
This paper proposes an intelligent segmentation technique for pineapple fruit using Convolutional Neural Network (CNN). Cascade Object Detector (COD) method is used to detect the position of the pineapple from the captured image by returning the bounding box around the detecting pineapple. Image background such as ground, sky and other unwanted objects have been removed using Hue value, Adaptive Red and Blue Chromatic Map (ARB) and Normalized Difference Index (NDI) methods. However, the ARB and NDI methods are still producing misclassified error and the edge is not really smooth. In this case Template Matching Method (TMM) has been implemented for image enhancement process. Finally, an intelligent CNN is developed as a decision maker to select the best segmentation image ouput from ARB and NDI. The results obtained show that the proposed intelligent method has successfully verified the fruit from the background with high accuracy as compared to the conventional method.