Oil palm fresh fruit bunch ripeness classification based on rule- based expert system of ROI image processing technique results

7th IGRSM International Remote Sensing & GIS Conference and Exhibition

地球科学;计算机科学

Alfatni, M.S.M.^1 ; Shariff, A.R.M.^1 ; Abdullah, M.Z.^2 ; Marhaban, M.H.^1 ; Shafie, S.B.^1 ; Bamiruddin, M.D.^3 ; Saaed, O.M.B.^1

Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia^1

School of Electrical and Electronic of Engineering, Universiti Sains Malaysia Engineering Campus, 14300, Seri Ampangan, Penang, Malaysia^2

MPOB, Persiaran Institusi, Bandar, Baru, Bangi, 43000 Kajang, Selangor, Malaysia^3

关键词: Classification system;    Colour features;    Image processing technique;    Inspection system;    K nearest neighbor (KNN);    Processing needs;    Region of interest;    Rule based expert systems;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/20/1/012018/pdf
DOI  :  10.1088/1755-1315/20/1/012018

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

There is a processing need for a fast, easy and accurate classification system for oil palm fruit ripeness. Such a system will be invaluable to farmers and plantation managers who need to sell their oil palm fresh fruit bunch (FFB) for the mill as this will avoid disputes. In this paper,a new approach was developed under the name of expert rules-based systembased on the image processing techniques results of thethree different oil palm FFB region of interests (ROIs), namely; ROI1 (300x300 pixels), ROI2 (50x50 pixels) and ROI3 (100x100 pixels). The results show that the best rule-based ROIs for statistical colour feature extraction with k-nearest neighbors (KNN) classifier at 94% were chosen as well as the ROIs that indicated results higher than the rule-based outcome, such as the ROIs of statistical colour feature extraction with artificial neural network (ANN) classifier at 94%, were selected for further FFB ripeness inspection system.