Global Thresholding based on Improved Histogram for Chalk area Segmentation in Rice Quality Evaluation

Khao Dawk Mali 105 (KDML105), internationally known as "Jasmine Rice", is one of the most famous and major commercial rice in Thailand. Physical appearance of polished grain is one of key factors that influences rice's price. One of major traits is a degree of chalkiness. Rice breeding scientists thus make a great effort in reducing grain chalkiness in order to meet market quality and match consumer preference. The routine task in breeding process is visually inspection of chalkiness level in rice. Since human visual inspection is slow, subjective, and not consistent over a long period, we propose to use global thresholding methods to automatically segment chalk area in order to improve speed of chalkiness inspection and provide objective and consistent results. However, due to the characteristics of rice chalk that causes several difficulties in a thresholding mechanism, we thus proposed a new method for improving histogram in order that the global threshold value can be computed easier. The proposed histogram improvement method has several desirable advantages, such as very low computational cost, efficiently dealing with the problem of low contrast, and insensitivity to size and location of objects. The effectiveness of the proposed histogram improvement method is evaluated using four well-known global thresholding methods on a real 96 chalky grain images with different degrees and variety characteristics of chalkiness. The accuracy of segmented chalk area was verified by comparing it with human segmentations produced by rice researchers. Experimental results demonstrate that the proposed histogram improvement method can significantly improve the segmentation results.