Detection of rice grain chalkiness level with volume estimation from image processing

2020 ◽  
Author(s):  
Achira Sujarit ◽  
Kunya Cheaupan ◽  
Nattaporn Chattham
Keyword(s):  
Image Processing ◽  
Volume Estimation ◽  
Rice Grain ◽  
Grain Chalkiness

Current status and strategies for improvement of rice grain chalkiness

2009 ◽  
Vol 31 (6) ◽  
pp. 563-572 ◽  
Author(s):  
Li-Jun ZHOU ◽  
Ling JIANG ◽  
Hu-Qu ZHAI ◽  
Jian-Min WAN
Keyword(s):  
Current Status ◽  
Rice Grain ◽  
Grain Chalkiness

scholarly journals Substitution Mapping of Two Closely Linked QTLs Controlling Grain Chalkiness and Grain Shape on Rice Chromosome 8

2021 ◽  
Author(s):  
Weifeng Yang ◽  
Liang Xiong ◽  
Jiayan Liang ◽  
Qingwen Hao ◽  
Xin Luan ◽  
...  
Keyword(s):  
Grain Quality ◽  
Grain Shape ◽  
Rice Chromosome ◽  
Chromosome 8 ◽  
High Yield ◽  
Rice Grain ◽  
Rice Varieties ◽  
Substitution Mapping ◽  
Grain Width ◽  
Grain Chalkiness

Abstract Background: Rice varieties are required to have high yield and good grain quality. Grain chalkiness and grain shape are two important traits of rice grain quality. Low chalkiness slender grains are preferred by most rice consumers. Here, we dissected two closely linked quantitative trait loci (QTLs) controlling grain chalkiness and grain shape on rice chromosome 8 by substitution mapping. Results: Two closely linked QTLs controlling grain chalkiness and grain shape were identified using single-segment substitution lines (SSSLs). The two QTLs were then dissected on rice chromosome 8 by secondary substitution mapping. qPGC8.1 was located in an interval of 1382.6 kb and qPGC8.2 was mapped in a 2057.1 kb region. The maximum distance of the two QTLs was 4.37 Mb and the space distance of two QTL intervals was 0.72 Mb. qPGC8.1 controlled grain chalkiness and grain width. qPGC8.2 was responsible for grain chalkiness and for grain length and grain width. The additive effects of qPGC8.1 and qPGC8.2 on grain chalkiness were not affected by heat stress. Conclusions: Two closely linked QTLs qPGC8.1 and qPGC8.2 were dissected on rice chromosome 8. They controlled the phenotypes of grain chalkiness and grain shape. The two QTLs were insensitive to high temperature.

Novel molecular and cell biological insights into function of rice α-amylase

Amylase ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Toshiaki Mitsui ◽  
Akihito Ochiai ◽  
Hiromoto Yamakawa ◽  
Kentaro Kaneko ◽  
Aya Kitajima-Koga ◽  
...  
Keyword(s):  
Intracellular Transport ◽  
Starch Synthesis ◽  
Grain Filling ◽  
Binding Pocket ◽  
Rice Grain ◽  
Active Site Cleft ◽  
Key Factor ◽  
Organ Specific ◽  
Basic Biology ◽  
Grain Chalkiness

Abstract α-Amylases have been of interest in diverse fields for many years because of their importance in basic biology, agriculture, and industry. Starch hydrolysis in plants has been studied extensively in germinating cereal seeds. It is generally accepted that α-amylases are secretory enzymes with a pivotal role in the breakdown of starch reserves in the endosperm. Intriguingly, however, recent investigations reveal that some α-amylases degrade starch in the plastids of living cells. The recent solving of the crystal structure of rice AmyI-1 isoform shows that the binding pocket of starch binding site 1 situated outside of the active site cleft interacts with the substances other than oligosaccharides. These findings provided novel insights into structural and cell biological aspects of α-amylase functions in intracellular transport, organelle targeting, and organ-specific actions. Under global warming, abnormal high temperatures during rice grain filling increase grain chalkiness, resulting in yield loss. Intensive “omics” analyses of developing caryopses and mature grains grown under heat stress showed the downregulation of starch synthesis enzymes and the upregulation of α-amylases. Transgenic studies using ectopic overexpression and suppression of α-amylase revealed that α-amylase is a key factor in grain chalkiness. Here we discuss unique new functions of α-amylase in rice cells.

Egg volume estimation based on image processing and computer vision

2020 ◽  
Vol 283 ◽  
pp. 110041 ◽  
Author(s):  
Cedric Okinda ◽  
Yuwen Sun ◽  
Innocent Nyalala ◽  
Tchalla Korohou ◽  
Samwel Opiyo ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Volume Estimation ◽  
Egg Volume

scholarly journals Proteomic analysis of proteins related to rice grain chalkiness using iTRAQ and a novel comparison system based on a notched-belly mutant with white-belly

2014 ◽  
Vol 14 (1) ◽  
pp. 163 ◽  
Author(s):  
Zhaomiao Lin ◽  
Xincheng Zhang ◽  
Xiaoyu Yang ◽  
Ganghua Li ◽  
She Tang ◽  
...  
Keyword(s):  
Proteomic Analysis ◽  
Rice Grain ◽  
Comparison System ◽  
Grain Chalkiness ◽  
White Belly

Aspect Ratio Analysis Using Image Processing for Rice Grain Quality

2010 ◽  
Vol 6 (5) ◽  
Author(s):  
Amit K Aggarwal ◽  
Ratan Mohan
Keyword(s):  
Image Processing ◽  
Aspect Ratio ◽  
Local Market ◽  
Rice Grain ◽  
Ratio Analysis ◽  
Ratio Distribution ◽  
Aspect Ratios ◽  
Percent Accuracy ◽  
Aspect Ratio Distribution

Determination of aspect ratio distribution is important for elongated, needle-shaped particles whose utility and/or value may depend on this feature. In this work rice grain is taken as an example of such a particle and its aspect ratio distribution in various samples is found using image processing. The samples examined were from three different grades (commonly termed as full, half and broken) sold in local market and priced according to their size. From the analysis, reference aspect ratios were assigned to classify the grains and hence determine the extent of off-size in each market grade. Further, the effectiveness of the technique to quantify mixed or adulterated grades was studied. It was found that it is possible to know the undesired content within 10 percent accuracy.

scholarly journals Rice Quality Analysis and Classification Using Image Processing Techniques

2021 ◽  
Vol 10 (6) ◽  
pp. 79-82
Author(s):  
Vijay Sonawane ◽  
Nikhil Gaikwad ◽  
Hrushikesh Mandekar ◽  
Kishore Baradkar ◽  
Chetan Gunjal
Keyword(s):  
Image Processing ◽  
Quality Analysis ◽  
World Population ◽  
Rice Grain ◽  
Rice Grains ◽  
Unique Shape ◽  
Good For ◽  
Phosphorus And Potassium ◽  
Processing Techniques

More than half the world's people consume rice every day and fulfills over 21% calorific requirement of world population. It is considered the whole grain which is rich in fiber and it contains 80 percent with protein, phosphorus, and potassium. There are hundreds of different varieties of rice and each rice grain has a unique shape, texture, and flavor that make it just right for certain dishes. The quality of rice between various types has different standards. Therefore, you must select the best quality rice because rice with best quality is not only good for consumption but also good for health. Analyzing grain sample manually is a tedious task and also time consuming. The paper presents the solution to analysis and grading of rice grains using image processing techniques. Image reduction, image enhancement, and image increment, object recognition in spatial domain is applied on grain by grain of different samples of rice to determine its size, color and quality as whole to grade the grain of rice. We find the endpoints of each grains and after we measure the length and breadth of rice grains.

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