The solution addressed the issue brought on by the structural similarity of edges by matching the
appropriate edge points based on the form of the edge rather than its texture. As a future scope authors
suggested to go with edge features extraction, with epipolar geometry to match the corresponding
points on the rice edge.
Avudaiappan and Sangamithra () analyse the visual features with image processing and MLP.
Authors used the SVM and Na¨ıve bays algorithm with 90% accuracy. As a future scope authors
focused on Non-Uniform Illumination with Transformation using top-hat so that rice can be classify in
long, normal or small category. Using a k-NN classifier, Wah, San, and Hlaing (2018) suggested an
image processing method and assessed three classes (30 images for each class).
Other studies Xiaopeng and Yong (2011), Yao, Chen, Guan, Sun, and Zhu (2009), Tahir, Hussin,
Htike, and Naing (2015) put more focus on identifying the grain’s apparent chalkiness. A grain with a
partly opaque or milky white kernel is said to be chalky. One of the key markers in the evaluation
process is the level of chalkiness. High levels of chalkiness in rice grains make them more likely to
shatter during milling, which will alter how they taste. An automated system for grading milled rice is
suggested by Wyawahare, Kulkarni, Dixit, and Marathe (2020).
Broken rice is an essential factor in rice grading. This technique may be used to determine the
percentage of broken rice from a sample’s picture. The relevant characteristics are retrieved from the
coloured pictures of the samples using particular preprogrammed processing procedures, and the
regression model is created. Estimating the percentage of broken rice requires less runtime than other
approaches since basic regression models are used. Lin, Li, Chen, and He (2018) offered a comparison
of two approaches—CNN and conventional methods—to identify rice grains with three distinct forms
(medium, round, and long grain). 5,554 photos were examined for calibration, and 1,845 images were
examined for validation. In the CNN approach, the experiments changed training parameters like
batch size and epochs. In a separate trial, they used conventional statistical techniques, and the
categorization accuracy they obtained ranged from 89 to 92%. As opposed to the conventional
approaches, the experiment employing the CNN method obtained a classification accuracy of 95.5%.
Benefits from the interplay between CNN and hyperspectral imaging were employed by
Chatnuntawech, Tantisantisom, Khanchaitit, Boonkoom, Bilgi¸c, and Chuangsuwanich (2018). Their
research used two sets of data, 414 samples from paddy rice and 232 samples from six different types
of milled rice. The accuracy of the suggested approach was 86.3%. In contrast, the SVM method on
the paddy seed dataset produced a result of 79.8%, whilst the accuracy of the other set was somewhat
off. By combining three machine learning techniques—kNN, SVM, and CNN—with hyper spectral
imagery, Qiu, Chen, Zhao, Zhu, He, and Zhang (2018) were able to identify four different types of rice
seeds. Two distinct spectral ranges were used in the experiment, and there were various numbers of
training samples.
In various studies, a hyperspectral camera was used to address the issue of classifying rice types. The
gadget, however, was expensive and complicated. Additionally, a quick computer, sensitive detectors,
and ample data storage were needed.
3. PROPOSED FRAMEWORK
By observing the various literature we Identified architecture. Process is as follows.
https://doi.org/10.17993/3ctic.2022.112.158-164
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