scholarly journals Classification of white rice grain quality using ANN: a review

2020 ◽  
Vol 9 (4) ◽  
pp. 600
Author(s):  
Anis Sufiya Hamzah ◽  
Azlinah Mohamed
Keyword(s):  
Grain Quality ◽  
Rice Grain ◽  
Advanced Technique ◽  
White Rice ◽  
Rice Grains ◽  
New Process ◽  
Artificial Neural Network Ann ◽  
Future Work ◽  
Stringent Quality

Exploring the new method of using technology for classifying rice grain quality is pertinent for rice producers in order to provide quality grains and protect consumers from any contamination exist. This is even more important when in today’s market we can see that rice with low quality is traded without stringent quality control which at the end will affect consumer’s health. This paper will review classification methods in determining quality white rice grain. Although there are many researchers developing new process to do rice classification by using different technique, there are still more advanced technique that can be used to do classification. This paper will focus on classifying rice grain quality using artificial neural network (ANN) approach with the help of image processing to identify the impurities contained in the rice grains. The findings show ANN using BPNN has the highest accuracy of 96%, it is also noted that other methods provide equally better performance. This review indicate hybrid method in ANN should be explored next for future work.

scholarly journals Analysis of Fungal Contamination on Commercially Sold Rice in Puerto Rico

2018 ◽  
Vol 15 (2) ◽  
Author(s):  
Nicole Colón Carrión ◽  
Chad Lozada Troche
Keyword(s):  
Rice Variety ◽  
Fungal Endophytes ◽  
Brown Rice ◽  
P Value ◽  
Rice Grain ◽  
Penicillium Verrucosum ◽  
Normal Surface ◽  
White Rice ◽  
Rice Grains ◽  
Fungal Contaminants

Crops and stored grains are susceptible to pathogens that represent a threat to our health. The study presented herein compares the normal surface and endophytic fungal communities present on white and brown rice grains. One hundred grains of each rice variety was analyzed to determine their fungal contaminants and endophytes. Fungi were inoculated on SDA media, and purified in PDA media; morphological characterization was performed followed by amplification of the ITS region using PCR for all fungal isolates. Statistical analysis indicated significant differences between medium brown rice compared to white rice for surface and endophytic communities (p-value £ 0.05). In addition, a higher fungal diversity was found on brown rice grains compared to white rice. This variation may be due to differences in the processing methods used for each rice grain type. BLAST analysis revealed the presence of toxigenic strains of Aspergillus flavus, A.oryzae, Penicillium verrucosum, and P. viridicatum. The study of fungal growth in rice grains can contribute to the minimization of mycotoxin production by its prevention and control; therefore, decreasing crop contamination and human exposure to their metabolites. KEYWORDS: Fungi; Rice; Fungal contaminants; Fungal endophytes

Laboratory Study of Vibrio cholerae O1 Survival on Three Types of Boiled Rice (Oryza sativa L.) Held at Room Temperature

2008 ◽  
Vol 71 (12) ◽  
pp. 2453-2459 ◽  
Author(s):  
JOHN TANG YEW HUAT ◽  
YAP KOK LEONG ◽  
HING HIANG LIAN
Keyword(s):  
Vibrio Cholerae ◽  
Room Temperature ◽  
Brown Rice ◽  
Rice Grain ◽  
White Rice ◽  
Rice Grains ◽  
Cooked Rice ◽  
Glutinous Rice ◽  
Vibrio Cholerae O1 ◽  
Grain Surface

This study examined whether the survival of Vibrio cholerae O1 on contaminated cooked rice was influenced by the type of rice. Vibrios survived unchanged on clumps of glutinous white rice (wet, grains adhered) held at room temperature for 24 h. On nonglutinous white rice (slightly moist, grains separate), 30% viable vibrios remained at 24 h. On nonglutinous brown rice (moist, separate, covered with a mucus-like substance), the number of vibrios increased 2.7-fold at 24 h. Survival rates of vibrios on the surfaces of a row of five cooked rice grains after 2 h of exposure at room temperature were 86, 29, 12, and 4% for glutinous rice, white rice, and the endosperm and pericarp of brown rice, respectively. (Each boiled brown rice grain surface was partly pericarp and partly endosperm, which became exposed by a rupture of the pericarp.) Covering each inoculated grain with a similar cooked rice grain surface increased the corresponding figures to 93, 99, 60, and 94%. Scanning electron microscopy revealed that each type of cooked grain surface possessed a distinct microtopography. For example, the surfaces of glutinous rice grains consisted of separated overlapping strips with many holes, while the pericarps of brown rice were flat interspersed with small pits. In conclusion, each type of boiled rice produced a distinct survival pattern of V. cholerae O1 caused by both the distinct gross features and the fine surface characteristics of the rice. The significance of this finding is that the type of rice consumed can be a factor in cholera transmission by contaminated rice.

scholarly journals Machine Vision Techniques for Improve Rice Grain Quality Analyzing Process

2020 ◽  
Vol 5 (6) ◽  
pp. 1001-1005
Author(s):  
G.M.K.B. Karunasena ◽  
H.D.N.S. Priyankara ◽  
B.G.D.A. Madushanka
Keyword(s):  
Aspect Ratio ◽  
Grain Quality ◽  
Low Cost ◽  
Maximum Length ◽  
Rice Grain ◽  
Maximum Width ◽  
Human Errors ◽  
Rice Grains ◽  
Human Quality ◽  
Percent Accuracy

Rice grain quality inspection is a major process in rice production. To provide quality and accurate results in rice grain analyzing it is important to analyze rice grains one by one in a testing sample. In the current situation, most of rice grain producers inspect rice grains manually without using any automated process. The major problem is the accuracy of testing results depends on human quality because manually processes include human errors. The manual inspection of rice grains is a very complicated and time-consuming process due to these reasons most of the inspector's effect by external factors such as fatigue, tension etc. In this research, we provide a time-efficient and low-cost solution for reducing above-mentioned limitations by developing software. It uses modern image processing to analyze rice grains one by one efficiently over the manual examination. The quality of rice samples can be determined with the help of colour, and geometric features such as area, maximum length, maximum width and aspect ratio. This analyzing system designed and developed for measure area, maximum length, maximum width and aspect ratio by using Java programming language, morphological and colour operations in computer vision and finally the accuracy of the system tested by comparing manually tested sample and results from the system. According to the results, it shows this system provides more than 85 percent accuracy with confirming this was a better solution

scholarly journals Efficacy of Irrigation Interval after Anthesis on Grain Quality, Alkali Digestion, and Gel Consistency of Rice

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 325
Author(s):  
Ramin Rayee ◽  
Tran Dang Xuan ◽  
Tran Dang Khanh ◽  
Hoang-Dung Tran ◽  
Kifayatullah Kakar
Keyword(s):  
Protein Content ◽  
Grain Quality ◽  
Cooking Quality ◽  
Amylose Content ◽  
Rice Grain ◽  
Gelatinization Temperature ◽  
Rice Varieties ◽  
Irrigation Interval ◽  
Gel Consistency ◽  
Alkali Digestion

The management of amylose and protein contents and cooking quality are the main challenges in rice macronutrients and quality improvement. This experiment was conducted to examine the rice grain quality, alkali digestion, and gel consistency responses to irrigation interval after anthesis. Three rice varieties (K1, K3, and K4) were subjected to different irrigation intervals (1, 2, and 3 d) after anthesis. The findings of this study showed that the protein content was markedly increased from 6.53–6.63% to 9.93–10.16%, whilst the amylose content was decreased significantly from 22.00–22.43% to 16.33–17.56% under stressed treatments at irrigation intervals, whilst the quantity of fatty acids was not affected. The 3-d irrigation interval recorded the highest protein content but the lowest amylose value. In addition, this treatment shows lower gelatinization temperature, but it is negatively associated with hard gel consistency under irrigation interval. This study highlights that the water management following a 3-d irrigation interval from anthesis is a useful and simple treatment to improve rice nutrients and grain cooking quality.

scholarly journals A hybrid CNN-LSTM model for pre-miRNA classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdulkadir Tasdelen ◽  
Baha Sen
Keyword(s):  
Evaluation Criteria ◽  
Mature Mirnas ◽  
Structural Features ◽  
Linear Discriminant ◽  
Sequential Structure ◽  
Precursor Mirnas ◽  
Lstm Network ◽  
Future Work ◽  
Cumulative Evidence

AbstractmiRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.

Novel Wx alleles generated by base editing for improvement of rice grain quality

2021 ◽  
Author(s):  
Xiaorui Huang ◽  
Fei Su ◽  
Sheng Huang ◽  
Fating Mei ◽  
Xiaomu Niu ◽  
...  
Keyword(s):  
Grain Quality ◽  
Rice Grain ◽  
Base Editing

scholarly journals A molecular switch in sulfur metabolism to reduce arsenic and enrich selenium in rice grain

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sheng-Kai Sun ◽  
Xuejie Xu ◽  
Zhong Tang ◽  
Zhu Tang ◽  
Xin-Yuan Huang ◽  
...  
Keyword(s):  
Grain Quality ◽  
Sulfur Metabolism ◽  
Physical Interaction ◽  
Rice Grain ◽  
Yield Losses ◽  
Serine Acetyltransferase ◽  
Cysteine Synthase ◽  
Arsenic Tolerance ◽  
Arsenic Accumulation ◽  
Low Levels

AbstractRice grains typically contain high levels of toxic arsenic but low levels of the essential micronutrient selenium. Anthropogenic arsenic contamination of paddy soils exacerbates arsenic toxicity in rice crops resulting in substantial yield losses. Here, we report the identification of the gain-of-function arsenite tolerant 1 (astol1) mutant of rice that benefits from enhanced sulfur and selenium assimilation, arsenic tolerance, and decreased arsenic accumulation in grains. The astol1 mutation promotes the physical interaction of the chloroplast-localized O-acetylserine (thiol) lyase protein with its interaction partner serine-acetyltransferase in the cysteine synthase complex. Activation of the serine-acetyltransferase in this complex promotes the uptake of sulfate and selenium and enhances the production of cysteine, glutathione, and phytochelatins, resulting in increased tolerance and decreased translocation of arsenic to grains. Our findings uncover the pivotal sensing-function of the cysteine synthase complex in plastids for optimizing stress resilience and grain quality by regulating a fundamental macronutrient assimilation pathway.

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