rice plant
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Abstract Background Small RNAs (sRNAs) regulate numerous plant processes directly related to yield, such as disease resistance and plant growth. To exploit this yield-regulating potential of sRNAs, the sRNA profile of one of the world’s most important staple crops – rice – was investigated throughout plant development using next-generation sequencing. Results Root and leaves were investigated at both the vegetative and generative phase, and early-life sRNA expression was characterized in the embryo and endosperm. This led to the identification of 49,505 novel sRNAs and 5581 tRNA-derived sRNAs (tsRNAs). In all tissues, 24 nt small interfering RNAs (siRNAs) were highly expressed and associated with euchromatic, but not heterochromatic transposable elements. Twenty-one nt siRNAs deriving from genic regions in the endosperm were exceptionally highly expressed, mimicking previously reported expression levels of 24 nt siRNAs in younger endosperm samples. In rice embryos, sRNA content was highly diverse while tsRNAs were underrepresented, possibly due to snoRNA activity. Publicly available mRNA expression and DNA methylation profiles were used to identify putative siRNA targets in embryo and endosperm. These include multiple genes related to the plant hormones gibberellic acid and ethylene, and to seed phytoalexin and iron content. Conclusions This work introduces multiple sRNAs as potential regulators of rice yield and quality, identifying them as possible targets for the continuous search to optimize rice production.

Abstract A Gram-stain-positive, aerobic, motile and rod-shaped bacterium, designated RG28T, was isolated from the roots of rice plant collected from paddy fields in Goyang, South Korea. Cells of the strain were oxidase-negative but catalase-positive. Strain RG28T was found to grow at 10–50°C (optimum, 25–30°C), pH 5.0–10.0 (optimum, pH 7.0) and in 1.0–5.0 % (w/v) NaCl (optimum, 0%). The cell-wall peptidoglycan contained meso-diaminopimelic acid and the predominant menaquinones were MK-7 and MK-6.The predominant cellular fatty acids were C16:0, iso-C15:0 and anteiso-C15:0. The major polar lipids included phosphatidylethanolamine, diphosphatidylglycerol, phosphatidylglycerol, four unidentified aminophosphoglycolipids, four unidentified aminophospholipids, two unidentified glycolipids, one unidentified aminoglycolipid and four unidentified lipids. The genomic DNA G+C content was 33.5 mol%. Phylogenetic analysis based on 16S rRNA gene sequences showed that the strain was closely related to Gottfriedia acidiceleris CBD 119T (98.6%), Gottfriedia solisilvae LMG 18422T (98.5 %) and Gottfriedia luciferensis LMG 18422T (98.4 %). The average nucleotide identity (ANI) and in silico DNA–DNA hybridization (isDDH) values between strain RG28T and type strains of Gottfriedia species were lower than the cut-offs (≥95–96 % for ANI and ≥70 % for isDDH) required to define a bacterial species. Meanwhile, the strain has the ability to produce indole-acetic acid (40.5 µg/mL). Phylogenetic, physiological and chemotaxonomic data suggested that strain RG28T represented a novel species of the genus Gottfriedia, for which the name Gottfriedia endophyticus sp. nov. is proposed, with the type strain RG28T (=KCTC 43327T=TBRC 15151T).Repositories: The draft genome and 16S rRNA gene sequences of strain RG28T have been deposited in GenBank/EMBL/DDBJ under accession numbers JAGIYQ000000000 and MW386408 respectively.

Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.

Rice is a staple food source for most countries in the world, including Indonesia. The problem of rice disease is a problem that is quite crucial and is experienced by many farmers. Approximately 200,000 - 300,000 tons per year the amount of rice attacked by pests in Indonesia. Considerable losses are caused by late-diagnosed rice plant diseases that reach a severe stage and cause crop failure. The limited number of Agricultural Extension Officers (PPL) and the Lack of information about disease and proper treatment are some of the causes of delays in handling rice diseases. Therefore, with the development of information technology and computers, it is possible to identify diseases by utilizing Artificial Intelligence, one of which is by using recognition methods based on image processing and pattern recognition technology. The purpose of this research is to create a Machine Learning model by applying the model architecture from Resnet101 combined with the model architecture from the author. The model proposed in this study produces an accuracy of 98.68%.

Rice plant is paddy producing plant needed by most of Indonesians as staple food. Sei Geringging Village in Kampar District, Kampar Kiri Sub-District had technically irrigated field on peat land and peat buried. Farmers on Sei Geringging Village farmed 2 kinds of rice which were prime variety and local variety. Most of prime variety farmed was Inpari 9 and the local variety was Mentik Wangi. Farmers on Sei Geringging Village had been farming the rice without knowing which variety had been best to farm on peat land and on peat buried that the yield had not been optimal. This research aimed to know the comparison of maximum growth and production rate of Inpari 9 and Mentik Wangi variety rice on peat land and peat buried. This research was done by a survey with descriptive method .Treatment combination was repeatedly done 4 times resulting 16 unit trial squares. Each square had 5 sample plants. Best growth was on peat land that planted with the Inpari 9 variety and on peat buried was planted with Mentik Wangi variety resulting yield of 4,20 ton.ha-1