Rice quality analysis using image processing techniques

Mahale, Bhagyashree; Korde, Sapana

doi:10.1109/i2ct.2014.7092300

“…Asif et al [15] used morphological features to determine the quality of five types of rice grains after a grain classification. Mahale and Korde [16] applied image processing techniques to grade and evaluate rice grains based on grain size and shape, such as length, width and their ratio. In contrast, Ali et al [17] proposed an low cost solution for rice quality analysis based on more features.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Defect Classification and Weight Estimation of Rice Kernels

Wang,

Li

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Rice is one of the main staple food in many areas of the world. The quality estimation of rice kernels are crucial in terms of both food safety and socio-economic impact. This was usually carried out by quality inspectors in the past, which may result in both objective and subjective inaccuracies. In this paper, we present an automatic visual quality estimation system of rice kernels, to classify the sampled rice kernels according to their types of flaws, and evaluate their quality via the weight ratios of the perspective kernel types. To compensate for the imbalance of different kernel numbers and classify kernels with multiple flaws accurately, we propose a multi-stage workflow which is able to locate the kernels in the captured image and classify their properties. We define a novel metric to measure the relative weight of each kernel in the image from its area, such that the relative weight of each type of kernels with regard to the all samples can be computed and used as the basis for rice quality estimation. Various experiments are carried out to show that our system is able to output precise results in a contactless way and replace tedious and error-prone manual works.

show abstract

“…The system was tested on a dataset of 300 rice images yielding an accuracy of 91% under a controlled laboratory environment. In the same year, Mahale and Korde introduced a semi-automatic approach based on the edge detection algorithm [82]. After extracting the rice boundaries, the manual measurement of rice length, breadth, and their ratio were made using Vernier Calipers and these were then used for classification.…”

Section: B Era 2 (2011-2016)mentioning

confidence: 99%

“…As was the case with rice breed classification, manual approaches for rice quality grading are too slow and have a greater chance for human error. It is therefore essential to automate the process [82]. In this regard, the approach proposed by Mahale and Korde used colored images of rice grains samples which were processed to remove noise using image morphological operations of dilation and erosion [32], followed by segmentation.…”

Section: Automated Grading Of Rice Grainsmentioning

confidence: 99%

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Ahmed

¹

,

Ali²,

Khan

³

2021

View full text Add to dashboard Cite

“…Manual rice quality estimation is quite tedious, as it requires experienced inspectors to identify and pick up the kernels with various defects one by one and weigh them carefully. The precision of the result is subject to the skill and conscientiousness of the inspectors [1].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Defect Classification and Weight Estimation of Rice Kernels

Wang

¹

,

Wang

²

,

Li

³

et al. 2022

IEEE Access

1

0

View full text Add to dashboard Cite

Rice is one of the main staple food in many areas of the world. The quality estimation of rice kernels are crucial in terms of both food safety and socio-economic impact. This was usually carried out by quality inspectors in the past, which may result in both objective and subjective inaccuracies. In this paper, we present an automatic visual quality estimation system of rice kernels, to classify the sampled rice kernels according to their types of flaws, and evaluate their quality via the weight ratios of the perspective kernel types. To compensate for the imbalance of different kernel numbers and classify kernels with multiple flaws accurately, we propose a multi-stage workflow which is able to locate the kernels in the captured image and classify their properties. We define a novel metric to measure the relative weight of each kernel in the image from its area, such that the relative weight of each type of kernels with regard to the all samples can be computed and used as the basis for rice quality estimation. Various experiments are carried out to show that our system is able to output precise results in a contactless way and replace tedious and error-prone manual works.INDEX TERMS Rice defect classification, rice quality estimation, rice weight estimation, vision-based method.

show abstract

Rice quality analysis using image processing techniques

Cited by 36 publications

References 2 publications

Vision-Based Defect Classification and Weight Estimation of Rice Kernels

Vision-Based Defect Classification and Weight Estimation of Rice Kernels

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Vision-Based Defect Classification and Weight Estimation of Rice Kernels

Contact Info

Product

Resources

About