Search Results (12)

In Asia, rice is the most consumed grain by humans, serving as a staple food in India. The yield of rice paddies is easily affected by nutrient deficiencies and leaf diseases. To overcome this problem and improve the yield productivity of rice, nutrient deficiency and leaf disease identification are essential. The main nutrient elements in paddies are potassium, phosphorus, and nitrogen (PPN), the deficiency of any of which strongly affects the rice plants. When multiple nutrient elements are deficient, the leaf color of the rice plants is altered. To overcome this problem, optimal nutrient delivery is required. Hence, the present study proposes the use of Fuzzy C Means clustering (FCM) with Improved Tunicate Swarm Optimization (ITSO) to segment the lesions in rice plant leaves and identify the deficient nutrients. The proposed ITSO integrates the Tunicate Swarm Optimization (TSO) and Bacterial Foraging Optimization (BFO) approaches. The Hybrid Convolutional Neural Network (HCNN), a deep learning model, is used with ITSO to classify the rice leaf diseases, as well as nutrient deficiencies in the leaves. Two datasets, namely, a field work dataset and a Kaggle dataset, were used for the present study. The proposed HCNN-ITSO classified Bacterial Leaf Bright (BLB), Narrow Brown Leaf Spot (NBLS), Sheath Rot (SR), Brown Spot (BS), and Leaf Smut (LS) in the field work dataset. Furthermore, the potassium-, phosphorus-, and nitrogen-deficiency-presenting leaves were classified using the proposed HCNN-ITSO in the Kaggle dataset. The MATLAB platform was used for experimental analysis in the field work and Kaggle datasets in terms of various performance measures. When compared to previous methods, the proposed method achieved the best accuracies of 98.8% and 99.01% in the field work and Kaggle datasets, respectively. Full article

Long non-coding RNAs (lncRNAs) regulate gene expression in eukaryotic organisms. Research suggests that lncRNAs may be involved in the regulation of nitrogen use efficiency in plants. In this study, we identified 1628 lncRNAs based on the transcriptomic sequencing of rice roots under low-nitrogen (LN) treatment through the implementation of an integrated bioinformatics pipeline. After 4 h of LN treatment, 50 lncRNAs and 373 mRNAs were significantly upregulated, and 17 lncRNAs and 578 mRNAs were significantly downregulated. After 48 h LN treatment, 43 lncRNAs and 536 mRNAs were significantly upregulated, and 42 lncRNAs and 947 mRNAs were significantly downregulated. Moreover, the interaction network among the identified lncRNAs and mRNAs was investigated and one of the LN-induced lncRNAs (lncRNA24320.6) was further characterized. lncRNA24320.6 was demonstrated to positively regulate the expression of a flavonoid 3′-hydroxylase 5 gene (OsF3′H5). The overexpression of lncRNA24320.6 was shown to improve nitrogen absorption and promote growth in rice seedlings under LN conditions. Our results provide valuable insights into the roles of lncRNAs in the rice response to nitrogen starvation. Full article

Rice blast is arguably the most devastating fungal disease of rice. Utilization of resistance genes to breed resistant cultivars is one of the most economical and environmentally friendly approaches to combat the disease. Pi25, a major resistance gene conferring broad-spectrum resistance to both leaf and neck blast, is an ideal gene resource to improve the resistance of rice varieties to blast. Recently, several allele-specific markers were developed. However, they were deficiently efficient due to either an additional process of restriction enzyme digestion for cleaved amplified polymorphic sequence (CAPS) markers or the risk of false-positive error in identifying susceptible Tetep allele (Pi25^TTP) for PCR-based markers. In this study, based on a conserved single nucleotide polymorphism (SNP) between resistant and susceptible alleles, a tetra-primer amplification refractory mutation system (ARMS)-PCR marker was developed. The new marker, namely Pi25-2687R3, could effectively distinguish the resistant Gumei 2 (GM2) allele (Pi25^GM2) and the susceptible allele Pi25^TTP. Moreover, a perfect consistency of genotyping was exhibited between Pi25-2687R3 and published CAPS marker CAP3/Hpy 99I. A more accurate genotyping was also displayed compared to the previous PCR-based SNP marker Pi25-2566. Our finding proved that Pi25-2687R3 could achieve the same result as CAP3/Hpy 99I with less workload and cost and could promote the accuracy in the identification of genotypes superior to Pi25-2566. This study provided a quick and reliable functional marker for discriminating Pi25 alleles, which would be a valuable tool for genotypic assay and rice molecular breeding of blast resistance. Full article

Low Phosphate Root (LPR) encodes a protein localized to the endoplasmic reticulum (ER) and cell wall. This gene plays a key role in responding to phosphate (Pi) deprivation, especially in remodeling the root system architecture (RSA). An identification and expression analysis of the OsLPR family in rice (Oryza sativa) has been previously reported, and OsLPR5, functioning in Pi uptake and translocation, is required for the normal growth and development of rice. However, the role of OsLPR3, one of the five members of this family in rice, in response to Pi deficiency and/or in the regulation of plant growth and development is unknown. Therefore, in this study, the roles of OsLPR3 in these processes were investigated, and some functions were found to differ between OsLPR3 and OsLPR5. OsLPR3 was found to be induced in the leaf blades, leaf sheaths, and roots under Pi deprivation. OsLPR3 overexpression strongly inhibited the growth and development of the rice but did not affect the Pi homeostasis of the plant. However, oslpr3 mutants improved RSA and Pi utilization, and they exhibited a higher tolerance to low Pi stress in rice. The agronomic traits of the oslpr3 mutants, such as 1000-grain weight and seed length, were stimulated under Pi-sufficient conditions, indicating that OsLPR3 plays roles different from those of OsLPR5 during plant growth and development, as well as in the maintenance of the Pi status of rice. Full article

Rice field sidewalk (RIFIS) identification plays a crucial role in enhancing the performance of agricultural computer applications, especially for rice farming, by dividing the image into areas of rice fields to be ploughed and the areas outside of rice fields. This division isolates the desired area and reduces computational costs for processing RIFIS detection in the automation of ploughing fields using hand tractors. Testing and evaluating the performance of the RIFIS detection method requires a collection of image data that includes various features of the rice field environment. However, the available agricultural image datasets focus only on rice plants and their diseases; a dataset that explicitly provides RIFIS imagery has not been found. This study presents an RIFIS image dataset that addresses this deficiency by including specific linear characteristics. In Bali, Indonesia, two geographically separated rice fields were selected. The initial data collected were from several videos, which were then converted into image sequences. Manual RIFIS annotations were applied to the image. This research produced a dataset consisting of 970 high-definition RGB images (1920 × 1080 pixels) and corresponding annotations. This dataset has a combination of 19 different features. By utilizing our dataset for detection, it can be applied not only for the time of rice planting but also for the time of rice harvest, and our dataset can be used for a variety of applications throughout the entire year. Full article

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%. Full article

Nitrogen (N) is an essential nutrient for plant growth and development; therefore, N deficiency is a major limiting factor in crop production. Plants have evolved mechanisms to cope with N deficiency, and the role of protein-coding genes in these mechanisms has been well studied. In the last decades, regulatory non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), small interfering RNAs (siRNAs), and long ncRNAs (lncRNAs), have emerged as important regulators of gene expression in diverse biological processes. Recent advances in technologies for transcriptome analysis have enabled identification of N-responsive ncRNAs on a genome-wide scale. Characterization of these ncRNAs is expected to improve our understanding of the gene regulatory mechanisms of N response. In this review, we highlight recent progress in identification and characterization of N-responsive ncRNAs in Arabidopsis thaliana and several other plant species including maize, rice, and Populus. Full article

Calcium (Ca²⁺) plays a critical role in the regulation of growth and development and environmental stress responses in plants. The membrane-associated Ca²⁺ transport proteins are required to mediate Ca²⁺ signaling and maintain Ca²⁺ homeostasis. Ca²⁺ channels, pumps (ATPases), and antiporters are three major classes of Ca²⁺ transporters. Although the genome-wide analysis of Ca²⁺ transporters in model plants Arabidopsis and rice have been well documented, the identification, classification, phylogenesis, expression profiles, and physiological functions of Ca²⁺ transport proteins in soybean are largely unknown. In this study, a comprehensive in silico analysis of gene families associated with Ca²⁺ transport was conducted, and a total of 207 putative Ca²⁺ transporter genes have been identified in soybean. These genes belong to nine different families, such as Ca²⁺-ATPase, Ca²⁺/cation antiporter, cyclic nucleotide-gated ion channel (CNGC), and hyperosmolality induced cytosolic Ca²⁺ concentration channel (OSCA). Detailed analysis of these identified genes was performed, including their classification, phylogenesis, protein domains, chromosomal distribution, and gene duplication. Expression profiling of these genes was conducted in different tissues and developmental stages, as well as under stresses using publicly available RNA-seq data. Some genes were found to be predominantly expressed in specific tissues like flowers and nodules, and some genes were found to be expressed strongly during seed development. Seventy-four genes were found to be significantly and differentially expressed under abiotic and biotic stresses, such as salt, phosphorus deficiency, and fungal pathogen inoculation. In addition, hormonal signaling- and stress response-related cis-elements and potential microRNA target sites were analyzed. This study suggests the potential roles of soybean Ca²⁺ transporters in stress responses and growth regulation, and provides a basis for further functional characterization of putative Ca²⁺ transporters in soybean. Full article

Phosphorus (P) deficiency is one of the major limiting factors for crop productivity. The yield of rice (Oryza sativa L.) is severely limited by phosphorus deficiency. An attempt has been made in this study to identify P deficiency responsive differentially expressed proteins of rice through analysis of leaf proteome of contrasting P-responsive rice cultivars under P deficiency conditions because genetic variability has been found in the rice cultivars for adaptive response to P deficiency and a controlled regulatory system is involved in the P deficiency adaptation response. Phosphorus-efficient (cv. Panvel) and P-inefficient (cv. Nagina 22) rice cultivars were hydroponically grown in the nutrient medium under control environmental conditions at low-P level (2.0 µM) and optimum-P level (320 µM) treatments. Expression patterns of the proteins of the leaves of both the cultivars were analyzed in 30-day-old plants. The identification of these proteins through mass spectrometry and MASCOT software (Matrix Science Inc., Boston, USA) revealed that these differentially expressed proteins were homologous to known functional proteins involved in energy metabolism, biosynthesis, photosynthesis, signaling, protein synthesis, protein folding, phospholipid metabolism, oxidative stress, transcription factors, and phosphorus metabolism. It has been observed that rice cultivars responded differently to low-P treatment through modification in protein expressions pattern to maintain the growth of the plants. Therefore, the expression patterns of proteins were different in both of the cultivars under low-P treatment. Higher potential of protein stability, stress tolerance, osmo-protection, and regulation of phosphorus uptake was observed in cv. Panvel than cv. Nagina 22. This study could help to unravel the complex regulatory process that is involved in adaptation to P deficiency in rice. Full article

Internode elongation is one of the key agronomic traits determining a plant’s height and biomass. However, our understanding of the molecular mechanisms controlling internode elongation is still limited in crop plant species. Here, we report the functional identification of an atypical basic helix-loop-helix transcription factor (OsbHLH073) through gain-of-function studies using overexpression (OsbHLH073-OX) and activation tagging (osbhlh073-D) lines of rice. The expression of OsbHLH073 was significantly increased in the osbhlh073-D line. The phenotype of osbhlh073-D showed semi-dwarfism due to deficient elongation of the first internode and poor panicle exsertion. Transgenic lines overexpressing OsbHLH073 confirmed the phenotype of the osbhlh073-D line. Exogenous gibberellic acid (GA₃) treatment recovered the semi-dwarf phenotype of osbhlh073-D plants at the seedling stage. In addition, quantitative expression analysis of genes involving in GA biosynthetic and signaling pathway revealed that the transcripts of rice ent-kaurene oxidases 1 and 2 (OsKO1 and OsKO2) encoding the GA biosynthetic enzyme were significantly downregulated in osbhlh073-D and OsbHLH073-OX lines. Yeast two-hybrid and localization assays showed that the OsbHLH073 protein is a nuclear localized-transcriptional activator. We report that OsbHLH073 participates in regulating plant height, internode elongation, and panicle exsertion by regulating GA biosynthesis associated with the OsKO1 and OsKO2 genes. Full article

Iron (Fe) deficiency and toxicity are the most widely prevalent soil-related micronutrient disorders in rice (Oryza sativa L.). Progress in rice cultivars with improved tolerance has been hampered by a poor understanding of Fe availability in the soil, the transportation mechanism, and associated genetic factors for the tolerance of Fe toxicity soil (FTS) or Fe deficiency soil (FDS) conditions. In the past, through conventional breeding approaches, rice varieties were developed especially suitable for low- and high-pH soils, which indirectly helped the varieties to tolerate FTS and FDS conditions. Rice-Fe interactions in the external environment of soil, internal homeostasis, and transportation have been studied extensively in the past few decades. However, the molecular and physiological mechanisms of Fe uptake and transport need to be characterized in response to the tolerance of morpho-physiological traits under Fe-toxic and -deficient soil conditions, and these traits need to be well integrated into breeding programs. A deeper understanding of the several factors that influence Fe absorption, uptake, and transport from soil to root and above-ground organs under FDS and FTS is needed to develop tolerant rice cultivars with improved grain yield. Therefore, the objective of this review paper is to congregate the different phenotypic screening methodologies for prospecting tolerant rice varieties and their responsible genetic traits, and Fe homeostasis related to all the known quantitative trait loci (QTLs), genes, and transporters, which could offer enormous information to rice breeders and biotechnologists to develop rice cultivars tolerant of Fe toxicity or deficiency. The mechanism of Fe regulation and transport from soil to grain needs to be understood in a systematic manner along with the cascade of metabolomics steps that are involved in the development of rice varieties tolerant of FTS and FDS. Therefore, the integration of breeding with advanced genome sequencing and omics technologies allows for the fine-tuning of tolerant genotypes on the basis of molecular genetics, and the further identification of novel genes and transporters that are related to Fe regulation from FTS and FDS conditions is incredibly important to achieve further success in this aspect. Full article

Non-destructive nutrition diagnosis provides effective technological support for agricultural sustainability. According to the plant nutrition mechanism, leaf characteristics displays different changing trends under nitrogen (N), phosphorus (P), and potassium (K) nutrition stress. In this study, the dynamic capture of rice leaf by scanning was used to research the changing regulation of leaf characteristics under nutrition stress. The leaf characteristics were extracted by mean value and regionprops functions in MATLAB, and the leaf dynamics were quantified by calculating the relative growth rate. Stepwise discriminant analysis and leave one out cross validation were applied to identify NPK deficiencies. The results indicated that leaves with N deficiency presented the lowest extension rate and the fastest wilt rate, followed by P and K deficiencies. During the identification, both morphological and color indices of the first incomplete leaf were effective indices for identification, but for the third fully expanded leaf, they were mainly color indices. Moreover, the first incomplete leaf had comparative advantage in early diagnosis (training accuracy 73.7%, validation accuracy 71.4% at the 26th day after transplantation), and the third fully expanded leaf generated higher accuracy at later stage. Overall, dynamic analysis expanded the application of leaf characteristics in identification, which contributes to improving the diagnostic effect. Full article