A hybrid intelligent approach based on computer vision and fuzzy logic for quality measurement of milled rice

• A hybrid intelligent system for quality measurement of milled rice is presented.
• The combination of machine vision and fuzzy logic is used for quality classification.
• Human experts’ judgments on quality grade of rice can be represented by the system.
• The proposed method successfully classifies different milled rice samples.
• The system can be utilized for automatic grading of rice in the processing industry.

In this research, a fuzzy inference system (FIS) coupled with image processing technique was developed as a decision-support system for qualitative grading of milled rice. Two quality indices, namely degree of milling (DOM) and percentage of broken kernels (PBK) were first graded by rice processing experts into five classes. Then, images of the same samples were captured using a machine vision system. The information obtained from the sample image processing was transferred to FIS. The FIS classifier consisted of two input linguistic variables, namely, DOM and PBK, and one output variable (Quality), all in the form of triangle membership functions. Altogether, 25 rules were considered in the FIS rule base using the AND operator and Mamdani inference system. In order to evaluate the developed system, statistical performance of the FIS classifier was compared with the experts’ judgments. Results of analysis showed a 89.8% agreement between the grading results obtained from the developed system and those determined by the experts.