Identification Of Rice Varieties Research Articles

Research on non-destructive identification technology of rice varieties based on HSI and GBDT

Accurate identification of rice varieties is of great significance for rice planting, field management and storage, and is also a key link in the process of agricultural breeding. In this study, a gradient boosting decision tree (GBDT) model was established based on hyperspectral imaging (HSI) to realize high-speed and non-destructive variety identification of six rice varieties. In this study, the near-infrared hyperspectral images of 600 rice samples of 6 varieties were taken as the research object, and the characteristic spectra of sensitive regions of the sample spectral images were processed by multiplicative scatter correction (MSC), and after the characteristic wavelengths were determined by the importance scores, the GBDT model to realize the identification of rice sample varieties, and the grid search algorithm was used to optimize the four internal parameters of GBDT. The results showed that the established GBDT model for the accuracy of rice variety identification of vitro test set samples reached 95%, indicating that HSI can be used to quickly and non-destructively identify rice varieties, and provide a new idea for batch online non-destructive testing of rice seeds.

A comparative analysis of deep learning and deep transfer learning approaches for identification of rice varieties

A comparative analysis of deep learning and deep transfer learning approaches for identification of rice varieties

Fine hyperspectral classification of rice varieties based on self-attention mechanism

The accurate identification of rice varieties using rapid and nondestructive hyperspectral technology is of practical significance for rice cultivation and agricultural production. This paper proposes a convolutional neural network classification model based on a self-attention mechanism (self-attention-1D-CNN) to improve accuracy in distinguishing between crop species in fields using canopy spectral information. After experimental materials were planted in the research area, portable equipment was used to collect the canopy hyperspectral data for rice during the booting stage. Five preprocessing methods and three extraction methods were used to process the data. A comparison of the classification accuracy of different classification models showed that the self-attention-1D-CNN proposed in this study achieved the best classification with an accuracy of 99.93%. The research demonstrated the feasibility of using hyperspectral technology for the fine classification of rice varieties, and the feasibility of using the CNN model as a potential classification method for near-ground crop monitoring and classification.

Rice Variety Identification Based on the Leaf Hyperspectral Feature via LPP-SVM

Rice variety identification is important for genetic breeding classification and crop yield estimation. Traditional identification methods are time-consuming and inaccurate. This paper proposes a method for rice variety identification based on the hyperspectral characteristics of leaves. Hyperspectral data of rice leaves were collected using a geophysical spectrometer imaging system. To reduce the redundance among the hyperspectral data and save the identification cost, locality preserving projections (LPP) is first applied to extract low-dimensional representative features from the leaf hyperspectral data. Then, support vector machine (SVM) is combined for conducting the identification of rice varieties. The experimental results show that the identification rate of 10 varieties of early rice was found to be 91.67% and the identification rate of 10 varieties of late rice was 97.33%.

Classification of Rice Varieties Using SIMCA Applied to NIR Spectroscopic Data.

The selection of suitable rice varieties is the key to achieve high and stable yields, and the correct identification of rice varieties is the prerequisite for seed selection. In this paper, with Kenjing No.5, No.6, and No.9 as the subjects, the effectiveness of near-infrared spectroscopy (NIRS) combined with soft independent modeling of class analogy (SIMCA) in the rapid identification of rice varieties was explored. The modeling sets of Kenjing No.5, No.6, and No.9 samples were respectively used to establish a SIMCA classification model based on principal component analysis (PCA). The accuracies of the model in classifying the rice samples in the modeling set were 100, 100, and 97.5%, respectively. Then, the established SIMCA model was used to identify the rice samples in the test set. According to the experimental findings, the SIMCA analytical method achieved 100% prediction accuracy for the Kenjing No.5, Kenjing No.6, and Hongyu 001-1 samples. For the Kenjing No.9 sample, the accuracy rate was 90% with a 10% sample of Kenjing No.9 misidentified as Kenjing No.6. Therefore, the analytical method of NIRS combined with SIMCA could effectively identify the rice varieties, providing a new approach for the correct selection of planting varieties.

Nondestructive Identification of Rice Varieties by the Data Fusion of Raman and Near-Infrared (NIR) Spectroscopies

The accurate classification of rice is important to effectively identify food fraud, especially in China, where rice is widely used as a staple. This study explored the application of near-infrared (NIR) and Raman spectroscopies and low-level and mid-level data fusion for the classification of four types of rice from similar origins. Classification models were constructed on separate data blocks after different preprocessing methods using partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM). Low-level data fusion spliced the two types of spectra and a classification technique was applied. Mid-level data fusion consisted of band selection and feature extraction from the spectra from each technique and the establishment of classification models. Based on fuzzy set theory, high-level data fusion was carried out for the results of PLS-DA models based on a single spectrum. The results show that mid-level data fusion is effective because its performance was superior to the use of one instrument. The highest accuracy was 87.5% for both NIR and Raman and 100% for the SVM model based on the mid-level data fusion. Mid-level data fusion of Raman and NIR spectroscopies is effective for the nondestructive identification of rice.

The Fraction of Intercepted Radiation to Nitrogen Absorption as an Indicator for Assessing Physiological Nitrogen Use Efficiency in Rice

Improving nitrogen (N) use efficiency is important for achieving sustainable rice production in China. Physiological N use efficiency (PNUE) is a measure of a plant’s ability to convert absorbed N into biomass, and can be calculated as the product of the fraction of intercepted radiation to N absorption (FIRNA) and radiation use efficiency (RUE). This study evaluated the relationships between PNUE with FIRNA and RUE in three widely grown, high-yielding rice varieties using data obtained from two N fertilization experiments conducted in 2020 and 2021. The results show that PNUE was significantly positively related to FIRNA, but not significantly related to RUE in all three rice varieties. PNUE increased by 7.4–10.3 g g−1 for each 10 MJ g−1 increase in FIRNA. These results suggest that FIRNA can serve as an indicator for assessing PNUE in rice, which has implications for the phenotypic identification of rice varieties with high PNUE.

Testing authenticity of certified paddy seed with user friendly Morpho-molecular tool- kit

In the present investigation, twenty nine rice cultivars were characterized using grain morphological characters and molecular markers to develop a morpho- molecular tool kit for application in authenticity testing of the certified seed lot and in maintenance breeding. The study suggested that identification of rice varieties is possible with grain morphology and molecular markers. Using grain morphological characters, seven varieties were uniquely identified viz., Prafulla, Numali, Dikhow, Dhanashree, Basundhara, Jalkuwari and Bhugali. Molecular fingerprints were generated based on SSR polymorphism. Using the present set of SSR markers, eight rice varieties viz., Gitesh, Jalashree, Ranjit, Jaymati, Bhugali, Ranjit sub-1, Numali, and Piyali, were distinguished with RM 12, RM 25, RM 190, RM 307 and RM 287on the basis of the unique banding pattern. In the phylogenetic relationship based on grain morphology, Dikhow was identified as an outlier, which is unique for the grain length, whereas based on SSR data, variety Praffulla was identified as an outlier with unique medium grain width. Key words: Rice, SSR marker, Diagnostic grain characters, Genetic purity &nbsp

Identification of Rice Varieties and Adulteration Using Gas Chromatography-Ion Mobility Spectrometry

To solve the problems existing in traditional biochemical methods, such as complex sample pretreatment requirements, tedious detection processes and low detection accuracies with respect to rice species and adulteration, the volatile flavor substances of five kinds of rice are detected using headspace-gas chromatography-ion mobility spectrometry (HGC-IMS) to effectively identify the quality of rice and adulterated rice. The ion migration fingerprint spectra of five kinds of rice are identified using a semi-supervised generative adversarial network (SSGAN). We replace the output layer of the discriminator in a GAN with a softmax classifier, thus extending the GAN to a semi-supervised GAN. We define additional category tags for generated samples to guide the training process. Semi-supervised training is used to optimize the network parameters, and the trained discriminant network is used for classifying HGC-IMS images. The experimental results show that the prediction accuracy of the model reaches 98.00%, which is significantly higher than the rates achieved by other models, such as a decision tree, a support vector machine (SVM), improved SVM models (LS-SVM and PCA-SVM) and local geometric structure Fisher analysis (LGSFA); 98.00% is also higher than the prediction accuracies of the VGGNet, ResNet and Fast RCNN deep learning models. The experimental results also show that the accuracy of HGC-IMS image classification for identifying adulterated rice reaches 97.30%, which is higher than those of traditional chromatographic or spectral methods. The proposed method overcomes the shortcomings of some intelligent algorithms regarding the application of ion migration spectra and is feasible for accurately predicting rice varieties and adulterated rice.

The Identification of Rice Varieties Used in Central Sulawesi

Central Sulawesi’s rice productivity in 2019 was lower compared to that in 2018. One of the problems for the low productivity of paddy in Central Sulawesi is the application of low quality of seeds. Ministry of Agriculture through Central Sulawesi AIAT has introduced a numbers of new high yielding varieties (HYV) to increase rice production and productivity. To support the dissemination of new HYV, it is important to study the rice varieties that mostly used by farmers in Central Sulawesi. The objectives of this study are to identify the rice varieties and the preferred characteristics of rice varieties that farmers usually used in Central Sulawesi. This study used primary and secondary data. Data were analyzed descriptively and presented in tables and graphs. The results show that most farmers in Central Sulawesi use Mekongga, Ciherang, and Cisantana varieties, and local varieties such as Peluncur, Dewi, Ntabone and others. Farmers generally prefer varieties which tend to produce higher yields and resistant to pests and diseases. To maintain the availability of the varieties in Central Sulawesi, it is important to train farmers to become breeders.

Improving Raman spectroscopic identification of rice varieties by feature extraction

AbstractRaman spectroscopy combined with pattern recognition can identify rice varieties excellently. In this study, a method was established to select the key feature for identification model. Seventy‐two Raman spectra of three varieties of rice were analyzed. Seventy‐one independent variables and characteristic bands (420–560, 820–980, 1,000–1,200, and 1,300–1,500 cm−1) were obtained by principal component analysis (PCA). Window analysis further narrowed the range of characteristic bands (451–550, 951–1,000, and 1,351–1,450 cm−1). Hierarchical cluster analysis (HCA) obtained 30 wavenumbers with small correlation. The prediction accuracy was 91.71%, whereas the time was reduced by 10 times when these 30 wavenumbers were used to establish the identification model. The method combined PCA, window analysis, and HCA with support vector machine can be used as an effective feature extraction method to improve the efficiency for identification of rice varieties. Under the circumstances of large sample size or relatively complex data, the screening of Raman spectrum information is an important means to simplify the model and improve the prediction efficiency.

Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics.

Background: In recent years, genetically modified technology has developed rapidly, and the potential impact of genetically modified foods on human health and the ecological environment has received increasing attention. The currently used methods for testing genetically modified foods are cumbersome, time-consuming, and expensive. This paper proposed a more efficient and convenient detection method. Methods: Near-infrared diffuse reflectance spectroscopy (NIRDRS) combined with multivariate calibration methods, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), were used for identification of different rice varieties and transgenic (Bt63)/non-transgenic rice. Spectral pretreatment methods, including Norris–Williams smooth (NWS), standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay 1st derivative (SG 1st-Der), were used for spectral noise reduction and effective information enhancement. Accuracy was used to evaluate the qualitative discriminant models. Results: The results showed that the SG 1st-Der pretreatment method, combined with the SVM, provided the optimal model to distinguish different rice varieties. The accuracy of the optimal model was 98.33%. For the discrimination model of transgenic/non-transgenic rice, the SNV-SVM model, MSC-SVM model, and SG 1st-Der-PLS-DA model all achieved good analysis results with the accuracy of 100%. Conclusion: The results showed that portable NIR spectroscopy combined with chemometrics methods could be used to identify rice varieties and transgenic characteristics (Bt63) due to its fast, non-destructive, and accurate advantages.

Vis/NIR reflectance spectroscopy for hybrid rice variety identification and chlorophyll content evaluation for different nitrogen fertilizer levels.

Nitrogen is one of the most important nutrient indicators for the growth of crops, and is closely related to the chlorophyll content of leaves and thus influences the photosynthetic ability of the crops. In this study, five hybrid rice varieties were cultivated during one entire growing period in one experimental field supplied with six nitrogen fertilizer levels. Visible and near infrared (vis/NIR) reflectance spectroscopy combined with multivariate analysis was used to identify hybrid rice varieties and nitrogen fertilizer levels, as well as to detect chlorophyll content associated with nitrogen levels. The support vector machine (SVM) algorithm was applied to identify five varieties of hybrid rice and six levels of nitrogen fertilizer. The results demonstrated that different varieties of hybrid rice for each nitrogen level can be well distinguished except for the highest nitrogen level, and no nitrogen level for each rice variety can be completely identified from the other five nitrogen levels. Further, 12 spectral indices combined with partial least square (PLS) analysis were applied for estimating chlorophyll content of rice leaves from plants subjected to different nitrogen levels, and a root mean square error of cross-validation (RMSECV) of 0.506, a coefficient of determination (R2) of 97.8% and a ratio of performance to deviation (RPD) of 4.6 for all rice varieties indicated this as a preferable procedure. This study demonstrates that Vis/NIR spectroscopy can have a great potential for identification of rice varieties and evaluation of nitrogen fertilizer levels.

Deciphering the non-coding RNA-level response to arsenic stress in rice (Oryza sativa)

ABSTRACTArsenic (As) contamination in subsoil and groundwater is a big problem, especially in many South-East Asian countries. As a staple crop growing under flooded condition in these areas, rice (Oryza sativa L.) becomes a big threat to human health through the food chain since As is highly accumulated in grains. Thus, reducing As accumulation in rice through molecular breeding and identification of rice varieties with low As content are the pressing issues. However, the current understanding on the molecular mechanism of As stress response is still limited for rice. In this study, we performed a comprehensive search for the As-responsive small RNAs (sRNAs) of rice. Briefly, 4,762 and 18,152 sRNAs were identified to be highly activated under As stress in roots and shoots respectively, while 14,603 and 8,308 sRNAs were intensively repressed by As treatment in roots and shoots, respectively. A number of the As-responsive sRNAs found their loci on tRNAs, rRNAs or long non-coding RNAs (lncRNAs). Interestingly, these loci preferentially distributed on the 5ʹ halves of the tRNA, rRNA or lncRNA precursors. Among the above-identified As-responsive sRNAs, 252 Argonaute 1 (AGO1)-enriched sRNAs were extracted for target identification, resulting in 200 pairs of sRNA–protein-coding target interactions. Many targets are functionally involved in the development, stress response, reproduction, or lipid metabolism. Additionally, 56 lncRNAs were discovered to be targeted by nine AGO1-enriched sRNAs, indicating the potential involvement of these lncRNAs in As signaling. Taken together, our results could expand the understanding on the non-coding RNA-mediated As stress response in rice.

Identification of rice varieties and determination of their geographical origin in China using Raman spectroscopy

The processing and quality properties of rice are significantly influenced by its variety and region of origin. However, discriminating between varieties and geographic regions is an urgent but difficult and time-consuming endeavor in China. In this study, an effective and reliable identification method was established by combining Raman spectroscopy (RS) with multivariate data analysis methods. Numerous RS spectra were collected, and the sensitive fundamental vibrations of less polar groups and bonds in rice were analyzed. Principal component analysis (PCA) was used for preliminary identification. Subsequently, different modeling methods were compared and seemed to reliably identify rice types, varieties, and region of origin, with accuracies of between 80 and 100%. As a result, a soft independent modeling of class analogy (SIMCA) model was shown to be the superior model for rice identification. The SIMCA model can deliver high precision detection of adulterated rice (i.e., rice of high quality blended with rice of inferior quality), and this study lays the foundations for an advanced rice quality identification technology system.

DNA Fingerprinting for Identification of Rice Varieties and Seed Genetic Purity Assessment

Maintenance of seed purity is an important skill-demanding aspect in seed production and multiplication. DNA fingerprinting allows identification of plant genotypes with high precision and stability as it is not influenced by environment, epistatic interactions and pleiotropic effects. DNA barcodes, the unique pattern of SSR polymorphism from the allelic variation data were developed for 14 visually similar varieties of medium grained rice and eight varieties of long slender grain types, having high market demand. Single-tube multiplex assays and DNA barcodes were generated using the available data on 32 markers for medium slender and 35 markers for long slender varieties to make variety identification easy and precise, which can supplement traditional standard practices in determining purity and certification.

Identification of rice varieties on the basis of seed characteristics through various chemical tests

An investigation was undertaken for identification of rice varieties on the basis of seed characteristics through various chemical tests. Seed samples of eleven rice cultivars (Subhadra, Sankar, Parijat, Suphala, Rudra, Kalinga-III, Khandagiri, Lalat, Bhuban, Sidhanta, Manaswini) which are under cultivation in the state of Odisha, were collected from Central Farm, Orissa University of Agriculture and Technology, Bhubaneswar and subjected to the following chemical tests viz., Phenol test, Modified phenol test (FeSO 4 and CuSO4), NaOH test and KOH test. One control was also taken in case of NaOH test and KOH test. Based on phenol colour reaction, Sidhanta, Lalat, Subhadra showed dark brown colour, where as Parijat, Sankar, Khandagiri, Kalinga-III showed light brown colour and rest four varieties exhibited no colour change. The modified phenol test using FeSOand CuSO4 solution helped in further sub-division of standard phenol group. In case of FeSO4 test, Parijat, Sankar, Rudra, Sidhanta, Bhuban showed dark brown colour and Kalinga-III, Khandagiri, Manaswini, Lalat, Subhadra, Suphala showed light brown colour. In case of CuSO 4 test, all the eleven varieties showed light brown colour. In case of NaOH test among eleven varieties Subhadra, Rudra, Khandagiri, Lalat, Sidhanta showed deep wine colour where as the other six varieties of rice showed wine colour. In case of KOH test except two varieties, Suphala and Khandagiri, other eight varieties showed deep wine colour. Control had no colour reaction in both NaOH and KOH test.

Fingerprinting Based on Microsatellite Markers for the Identification of Rice Varieties in Chhattisgarh

Molecular markers are useful tools for determining cultivar identity. The purpose of this study to establish fingerprints of four rice varieties in Chhattisgarh. Microsatellite combines several features of an ultimate molecular marker and they are being used increasingly in various plant genetic studies and applications. The varieties were evaluated utilizing 115 microsatellite markers distributed over the whole rice genome. A total of 160 alleles were detected using 24 microsatellite primer-pairs, and the number of alleles/marker ranged from 2 to 4 with an average of 2.95 alleles/locus. The result suggested that a relatively rapid and small number of microsatellite markers could be used for the identification of rice varieties.

Identification of Rice Varieties with High Tolerance or Sensitivity to Copper

ABSTRACT To study tolerance of rice (Oryza sativa L.) to excessive copper (Cu), 167 varieties were screened for Cu tolerance or sensitivity. Based on the elongation of the root, No. 1139 and No. 1195 were chosen as Cu-tolerant and Cu-sensitive variety. Compared with the control (0.32 μ mol L− 1 Cu), treatments with 5 and 8 μ mol L− 1 Cu for 10 days had no significant effect on the root and shoot dry masses of Cu-tolerant variety No. 1139, but significantly decreased root and shoot dry masses of variety No. 1195. Copper at 5 and 8 μ mol L− 1 also significantly decreased contents of chlorophyll and carotenoid and the ratio of variable to maximal chlorophyll fluorescence (Fv/Fm) in the leaves of the Cu-sensitive variety No. 1195. Roots and shoots of variety No. 1139 contained significantly higher the concentration of Cu than variety No. 1195 exposed to 5 and 8 μ mol L− 1 Cu. However, variety No. 1139 had higher Cu proportions on the cell walls in shoots and roots than variety No. 1195.

Simple sequence repeat (SSR) marker를 이용한 벼 품종 식별

The objective of this study was carried out to evaluate the suitability of simple sequence repeat (SSR) markers for genetic diversity assessment and identification of rice varieties. The 23 primers selected from 50 SSR primers showed polymorphisms in the 21 rice varieties. The 2 to 9 SSR alleles were detected for each locus with an average of 3.00 alleles per locus. The polymorphism information content (PIC) ranged from 0.091 to 0.839. Based on band patterns, UPGMA cluster analysis was conducted. These varieties were separated into 4 groups corresponding to rice ecotype and pedigree information and genetic distance of cluster ranging from 0.59 to 0.92. The 4 SSR primer sets (RM206, RM225, RM418, RM478) selected from 23 polymorphic primers were differentiated all the rice variety from each other by markers genotypes. These markers may be used wide range of practical application in variety identification of rice.