Rice Dataset Research Articles

Fine classification of rice fields in high-resolution remote sensing images

Fine-grained management of rice fields can enhance the yield and quality of rice crops. Challenges in achieving fine classification include interference from similar vegetation, the irregularity of natural field shapes, and complex scale variations. This paper introduces Rice Attention Cascade Network (RACNet), for the fine classification of rice fields in high-resolution satellite remote sensing imagery. The network employs the Hybrid Task Cascade network as the base framework and uses spectral and indices mixed multimodal data as input to reinforce the feature differentiation of similar vegetation. Initially, a Channel Attention Deformable-ResNet (CAD-ResNet) was designed to enhance the feature representation of rice on different channels. Deformable convolution improves the ability of CAD-ResNet to capture irregular field shapes. Then, to address the issue of complex scale changes, the multi-scale features extracted by the CAD-ResNet are progressively fused using an Asymptotic Feature Pyramid, reducing the loss of scale information between non-adjacent layers. Experiments on the Meishan rice dataset show that the proposed method is capable of accurate instance segmentation for fragmented or irregularly shaped rice fields. The evaluation metric AP50 of RACNet reaches 50.8%.

Optimized Transfer Learning for Chlorophyll Content Estimations across Datasets of Different Species Using Sun-Induced Chlorophyll Fluorescence and Reflectance

Remote sensing-based techniques have been widely used for chlorophyll content (Cab) estimations, while they are challenging when transferred across different species. Sun-induced chlorophyll fluorescence (SIF) provides a new approach to address these issues. This research explores whether SIF has transferability for Cab estimation and to enhance between-species transferability. Here, three rice datasets and a rapeseed dataset were collected. Initially, direct transfer models were constructed using partial least squares regression (PLSR) based on SIF yield (SIFY) and reflectance, respectively. Subsequently, methods were employed within the rice datasets to improve the models’ transferability. Finally, the between-species transferability of two data sources was validated in the rapeseed dataset. Direct transfer models indicated that the reflectance-based model exhibited a higher accuracy in predicting Cab when the training dataset acquired sufficient features, whereas the SIFY-based model showed better performance with fewer features. Spectral preprocessing methods can enhance the transferability, especially for SIFY-based models. In addition, supplementing 10% of out-of-sample data significantly improved the transferability. The proposed methods only require a small amount of new data to extend the original model for predicting Cab in other species. Specifically, the new method reduced the average RMSE based on SIFY and reflectance models by 23.59% and 35.51%, respectively.

An Advanced CNN based System for Rice Plant Diseases Detection and classification in Precision Agriculture

For the vast majority of people on the planet, agriculture is one of the most crucial industries. Rice is a vital food source, but it also faces serious risks from illnesses that can affect yield quality and quantity, potentially resulting in 20% to 40% crop losses. Reducing production losses requires early diagnosis of these disorders. For farmers, however, it is not possible to manually monitor such large areas of field. The proposed model based on a Convolutional Neural Network of Resnet50 and transfer learning model, specifically a modified TLResnet152V2. Transfer learning-based weights are used for precise identification and categorization and detection for rice leaf diseases. The proposed system demonstrates high accuracy in identifying four classes of rice diseases viz: Brown spot, Healthy, LeafBlast, NeckBlast. It achieves a remarkable accuracy of Resnet50 & TLResnet152V2 is 93.20% and 95.03 % for normalized augmented dataset respectively. Comparative analysis reveals that modified approach outperforms similar methodologies applied to the Bangladesh rice dataset or datasets of comparable size given in the existing research work. Overall findings underscore the efficacy of proposed approach in advancing the precision of rice plant disease detection, offering a promising solution to enhance agricultural practices and mitigate production losses.

BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies.

Multilocus genome-wide association study has become the state-of-the-art tool for dissecting the genetic architecture of complex and multiomic traits. However, most existing multilocus methods require relatively long computational time when analyzing large datasets. To address this issue, in this study, we proposed a fast mrMLM method, namely, best linear unbiased prediction multilocus random-SNP-effect mixed linear model (BLUPmrMLM). First, genome-wide single-marker scanning in mrMLM was replaced by vectorized Wald tests based on the best linear unbiased prediction (BLUP) values of marker effects and their variances in BLUPmrMLM. Then, adaptive best subset selection (ABESS) was used to identify potentially associated markers on each chromosome to reduce computational time when estimating marker effects via empirical Bayes. Finally, shared memory and parallel computing schemes were used to reduce the computational time. In simulation studies, BLUPmrMLM outperformed GEMMA, EMMAX, mrMLM, and FarmCPU as well as the control method (BLUPmrMLM with ABESS removed), in terms of computational time, power, accuracy for estimating quantitative trait nucleotide positions and effects, false positive rate, false discovery rate, false negative rate, and F1 score. In the reanalysis of two large rice datasets, BLUPmrMLM significantly reduced the computational time and identified more previously reported genes, compared with the aforementioned methods. This study provides an excellent multilocus model method for the analysis of large-scale and multiomic datasets. The software mrMLM v5.1 is available at BioCode (https://ngdc.cncb.ac.cn/biocode/tool/BT007388) or GitHub (https://github.com/YuanmingZhang65/mrMLM).

HyA-GAN: remote sensing image cloud removal based on hybrid attention generation adversarial network

ABSTRACT In recent years, with the continuous advancement of remote sensing technology, remote sensing images have found widespread application in various fields. However, remote sensing images are often hindered by the presence of clouds, which can significantly reduce their usability. Removing clouds from remote sensing images has thus become an essential preprocessing step in remote sensing image analysis. While numerous models based on deep learning have produced impressive results in the domain of image denoising, there has been limited research on applying deep neural networks to tackle the issue of cloud removal in remote sensing images. This paper proposes a novel model, the Hybrid Attention Generative Adversarial Network (HyA-GAN), that blends the channel attention mechanism and the spatial attention mechanism and integrates it into a generative adversarial network. By incorporating both the channel attention mechanism and the spatial attention mechanism, HyA-GAN permits the network to prioritize regions that are vital for image processing. This feature leads to an improvement in the network’s capacity to recover data and generate remote sensing images with superior cloud removal effects. When comparing HyA-GAN with other existing cloud removal models on the RICE dataset, it outperformed them in both peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The experiments demonstrate the high potential of HyA-GAN in addressing the challenge of removing clouds from remote sensing images.

PMVT: a lightweight vision transformer for plant disease identification on mobile devices.

Due to the constraints of agricultural computing resources and the diversity of plant diseases, it is challenging to achieve the desired accuracy rate while keeping the network lightweight. In this paper, we proposed a computationally efficient deep learning architecture based on the mobile vision transformer (MobileViT) for real-time detection of plant diseases, which we called plant-based MobileViT (PMVT). Our proposed model was designed to be highly accurate and low-cost, making it suitable for deployment on mobile devices with limited resources. Specifically, we replaced the convolution block in MobileViT with an inverted residual structure that employs a 7×7 convolution kernel to effectively model long-distance dependencies between different leaves in plant disease images. Furthermore, inspired by the concept of multi-level attention in computer vision tasks, we integrated a convolutional block attention module (CBAM) into the standard ViT encoder. This integration allows the network to effectively avoid irrelevant information and focus on essential features. The PMVT network achieves reduced parameter counts compared to alternative networks on various mobile devices while maintaining high accuracy across different vision tasks. Extensive experiments on multiple agricultural datasets, including wheat, coffee, and rice, demonstrate that the proposed method outperforms the current best lightweight and heavyweight models. On the wheat dataset, PMVT achieves the highest accuracy of 93.6% using approximately 0.98 million (M) parameters. This accuracy is 1.6% higher than that of MobileNetV3. Under the same parameters, PMVT achieved an accuracy of 85.4% on the coffee dataset, surpassing SqueezeNet by 2.3%. Furthermore, out method achieved an accuracy of 93.1% on the rice dataset, surpassing MobileNetV3 by 3.4%. Additionally, we developed a plant disease diagnosis app and successfully used the trained PMVT model to identify plant disease in different scenarios.

Stacking-based and improved convolutional neural network: a new approach in rice leaf disease identification.

Rice leaf diseases are important causes of poor rice yields, and accurately identifying diseases and taking corresponding measures are important ways to improve yields. However, rice leaf diseases are diverse and varied; to address the low efficiency and high cost of manual identification, this study proposes a stacking-based integrated learning model for the efficient and accurate identification of rice leaf diseases. The stacking-based integrated learning model with four convolutional neural networks (namely, an improved AlexNet, an improved GoogLeNet, ResNet50 and MobileNetV3) as the base learners and a support vector machine (SVM) as the sublearner was constructed, and the recognition rate achieved on a rice dataset reached 99.69%. Different improvement methods have different effects on the learning and training processes for different classification tasks. To investigate the effects of different improvement methods on the accuracy of rice leaf disease diagnosis, experiments such as comparison experiments between single models and different stacking-based ensemble model combinations and comparison experiments with different datasets were executed. The model proposed in this study was shown to be more effective than single models and achieved good results on a plant dataset, providing a better method for plant disease identification.

A Cross-Modal Feature Fusion Model Based on ConvNeXt for RGB-D Semantic Segmentation

Semantic segmentation, as the pixel level classification with dividing an image into multiple blocks based on the similarities and differences of categories (i.e., assigning each pixel in the image to a class label), is an important task in computer vision. Combining RGB and Depth information can improve the performance of semantic segmentation. However, there is still a problem of the way to deeply integrate RGB and Depth. In this paper, we propose a cross-modal feature fusion RGB-D semantic segmentation model based on ConvNeXt, which uses ConvNeXt as the skeleton network and embeds a cross-modal feature fusion module (CMFFM). The CMFFM designs feature channel-wise and spectral-wise fusion, which can realize the deeply feature fusion of RGB and Depth. The in-depth multi-modal feature fusion in multiple stages improves the performance of the model. Experiments are performed on the public dataset of SUN-RGBD, showing the best segmentation by our proposed model ConvNeXt-CMFFM with the highest mIoU score of 53.5% among the nine comparative models. The outstanding performance of ConvNeXt-CMFFM is also achieved on our self-built dataset of RICE-RGBD with the highest mIoU score and pixel accuracy among the three comparative datasets. The ablation experiment on our rice dataset shows that compared with ConvNeXt (without CMFFM), the mIoU score of ConvNext-CMFFM is increased from 71.5% to 74.8% and its pixel accuracy is increased from 86.2% to 88.3%, indicating the effectiveness of the added feature fusion module in improving segmentation performance. This study shows the feasibility of the practical application of the proposed model in agriculture.

A Method of Polished Rice Image Segmentation Based on YO-LACTS for Quality Detection

The problem of small and multi-object polished rice image segmentation has always been one of importance and difficulty in the field of image segmentation. In the appearance quality detection of polished rice, image segmentation is a crucial part, directly affecting the results of follow-up physicochemical indicators. To avoid leak detection and inaccuracy in image segmentation qualifying polished rice, this paper proposes a new image segmentation method (YO-LACTS), combining YOLOv5 with YOLACT. We tested the YOLOv5-based object detection network, to extract Regions of Interest (RoI) from the whole image of the polished rice, in order to reduce the image complexity and maximize the target feature difference. We refined the segmentation of the RoI image by establishing the instance segmentation network YOLACT, and we eventually procured the outcome by merging the RoI. Compared to other algorithms based on polished rice datasets, this constructed method was shown to present the image segmentation, enabling researchers to evaluate polished rice satisfactorily.

Two-Stage Intelligent DarkNet-SqueezeNet Architecture-Based Framework for Multiclass Rice Grain Variety Identification.

Image processing is an important domain for identifying various crop varieties. Due to the large amount of rice and its varieties, manually detecting its qualities is a very tedious and time-consuming task. In this work, we propose a two-stage deep learning framework for detecting and classifying multiclass rice grain varieties. A series of steps is included in the proposed framework. The first step is to perform preprocessing on the selected dataset. The second step involves selecting and fine-tuning pretrained deep models from Darknet19 and SqueezeNet. Transfer learning is used to train the fine-tuned models on the selected dataset. The 50% sample images are employed for the training and rest 50% are used for the testing. Features are extracted and fused using a maximum correlation-based approach. This approach improved the classification performance; however, redundant information has also been included. An improved butterfly optimization algorithm (BOA) is proposed, in the next step, for the selection of the best features that are finally classified using several machine learning classifiers. The experimental process was conducted on selected rice datasets that include five types of rice varieties and achieves a maximum accuracy of 100% that was improved than the recent method. The average accuracy of the proposed method is obtained at 99.2%, through confidence interval-based analysis that shows the significance of this work.

Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation

Rice yield is closely related to the number and proportional area of rice panicles. Currently, rice panicle information is acquired with manual observation, which is inefficient and subjective. To solve this problem, we propose an improved Mask R-CNN combined with Otsu preprocessing for rice detection and segmentation. This method first constructs a rice dataset for rice images in a large field environment, expands the dataset using data augmentation, and then uses LabelMe to label the rice panicles. The optimized Mask R-CNN is used as a rice detection and segmentation model. Actual rice panicle images are preprocessed by the Otsu algorithm and input into the model, which yields accurate rice panicle detection and segmentation results using the structural similarity and perceptual hash value as the measurement criteria. The results show that the proposed method has the highest detection and segmentation accuracy for rice panicles among the compared algorithms. When further calculating the number and relative proportional area of the rice panicles, the average error of the number of rice panicles is 16.73% with a minimum error of 5.39%, and the error of the relative proportional of rice panicles does not exceed 5%, with a minimum error of 1.97% and an average error of 3.90%. The improved Mask R-CNN combined with Otsu preprocessing for rice panicle detection and segmentation proposed in this paper can operate well in a large field environment, making it highly suitable for rice growth monitoring and yield estimation.

Heritability estimation for a linear combination of phenotypes via ridge regression.

The joint analysis of multiple phenotypes is important in many biological studies, such as plant and animal breeding. The heritability estimation for a linear combination of phenotypes is designed to account for correlation information. Existing methods for estimating heritability mainly focus on single phenotypes under random-effect models. These methods also require some stringent conditions, which calls for a more flexible and interpretable method for estimating heritability. Fixed-effect models emerge as a useful alternative. In this article, we propose a novel heritability estimator based on multivariate ridge regression for linear combinations of phenotypes, yielding accurate estimates in both sparse and dense cases. Under mild conditions in the high-dimensional setting, the proposed estimator appears to be consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is promising under different scenarios. Compared with independently combined heritability estimates in the case of multiple phenotypes, the proposed method significantly improves the performance by considering correlations among those phenotypes. We further demonstrate its application in heritability estimation and correlation analysis for the Oryza sativa rice dataset. An R package implementing the proposed method is available at https://github.com/xg-SUFE1/MultiRidgeVar, where covariance estimates are also given together with heritability estimates. Supplementary data are available at Bioinformatics online.

IEnhancer-DLRA: identification of enhancers and their strengths by a self-attention fusion strategy for local and global features.

Identification and classification of enhancers are highly significant because they play crucial roles in controlling gene transcription. Recently, several deep learning-based methods for identifying enhancers and their strengths have been developed. However, existing methods are usually limited because they use only local or only global features. The combination of local and global features is critical to further improve the prediction performance. In this work, we propose a novel deep learning-based method, called iEnhancer-DLRA, to identify enhancers and their strengths. iEnhancer-DLRA extracts local and multi-scale global features of sequences by using a residual convolutional network and two bidirectional long short-term memory networks. Then, a self-attention fusion strategy is proposed to deeply integrate these local and global features. The experimental results on the independent test dataset indicate that iEnhancer-DLRA performs better than nine existing state-of-the-art methods in both identification and classification of enhancers in almost all metrics. iEnhancer-DLRA achieves 13.8% (for identifying enhancers) and 12.6% (for classifying strengths) improvement in accuracy compared with the best existing state-of-the-art method. This is the first time that the accuracy of an enhancer identifier exceeds 0.9 and the accuracy of the enhancer classifier exceeds 0.8 on the independent test set. Moreover, iEnhancer-DLRA achieves superior predictive performance on the rice dataset compared with the state-of-the-art method RiceENN.

Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species

DNA N6-methyladenine (6mA) is a key DNA modification, which plays versatile roles in the cellular processes, including regulation of gene expression, DNA repair, and DNA replication. DNA 6mA is closely associated with many diseases in the mammals and with growth as well as development of plants. Precisely detecting DNA 6mA sites is of great importance to exploration of 6mA functions. Although many computational methods have been presented for DNA 6mA prediction, there is still a wide gap in the practical application. We presented a convolution neural network (CNN) and bi-directional long-short term memory (Bi-LSTM)-based deep learning method (Deep6mAPred) for predicting DNA 6mA sites across plant species. The Deep6mAPred stacked the CNNs and the Bi-LSTMs in a paralleling manner instead of a series-connection manner. The Deep6mAPred also employed the attention mechanism for improving the representations of sequences. The Deep6mAPred reached an accuracy of 0.9556 over the independent rice dataset, far outperforming the state-of-the-art methods. The tests across plant species showed that the Deep6mAPred is of a remarkable advantage over the state of the art methods. We developed a user-friendly web application for DNA 6mA prediction, which is freely available at http://106.13.196.152:7001/ for all the scientific researchers. The Deep6mAPred would enrich tools to predict DNA 6mA sites and speed up the exploration of DNA modification.

I6mA-Caps: a CapsuleNet-based framework for identifying DNA N6-methyladenine sites.

DNA N6-methyladenine (6mA) has been demonstrated to have an essential function in epigenetic modification in eukaryotic species in recent research. 6mA has been linked to various biological processes. It's critical to create a new algorithm that can rapidly and reliably detect 6mA sites in genomes to investigate their biological roles. The identification of 6mA marks in the genome is the first and most important step in understanding the underlying molecular processes, as well as their regulatory functions. In this article, we proposed a novel computational tool called i6mA-Caps which CapsuleNet based a framework for identifying the DNA N6-methyladenine sites. The proposed framework uses a single encoding scheme for numerical representation of the DNA sequence. The numerical data is then used by the set of convolution layers to extract low-level features. These features are then used by the capsule network to extract intermediate-level and later high-level features to classify the 6mA sites. The proposed network is evaluated on three datasets belonging to three genomes which are Rosaceae, Rice and Arabidopsis thaliana. Proposed method has attained an accuracy of 96.71%, 94% and 86.83% for independent Rosaceae dataset, Rice dataset and A.thaliana dataset respectively. The proposed framework has exhibited improved results when compared with the existing top-of-the-line methods. A user-friendly web-server is made available for the biological experts which can be accessed at: http://nsclbio.jbnu.ac.kr/tools/i6mA-Caps/. Supplementary data are available at Bioinformatics online.

An Attention Encoder-Decoder Network Based on Generative Adversarial Network for Remote Sensing Image Dehazing

Remote sensing image dehazing is a difficult problem for its complex characteristics. It can be regarded as the preprocessing of high-level tasks of remote sensing images. To remove haze from the hazy remote sensing image, an encoder-decoder based on generative adversarial network is proposed. It first learns the low-frequency information of the image, and then learns the high-frequency information of the image. The skip connection is also added in the network to avoid losing information. To further improve the ability of learning more useful information, a multi-scale attention module is proposed. Meanwhile, a CBlock module is also designed to extract more feature information. It can capture different size of receptive fields. In order to reduce the computational pressure of the network, a distillation module is used in the network. Inspired by multi-scale network, an enhance module is designed and introduced it in the end of the network to further improve the dehazing ability of the network by integrating context information on multi-scale. We compared with five methods and our proposed method on RICE dataset. Experimental results show that our method achieves the best effect, both qualitatively and quantitatively.

Utilization Strategies of Two Environment Phenotypes in Genomic Prediction.

Multiple environment phenotypes may be utilized to implement genomic prediction in plant breeding, while it is unclear about optimal utilization strategies according to its different availability. It is necessary to assess the utilization strategies of genomic prediction models based on different availability of multiple environment phenotypes. Here, we compared the prediction accuracy of three genomic prediction models (genomic prediction model (genomic best linear unbiased prediction (GBLUP), genomic best linear unbiased prediction (GFBLUP), and multi-trait genomic best linear unbiased prediction (mtGBLUP)) which leveraged diverse information from multiple environment phenotypes using a rice dataset containing 19 agronomic traits in two disparate seasons. We found that the prediction accuracy of genomic prediction models considering multiple environment phenotypes (GFBLUP and mtGBLUP) was better than the classical genomic prediction model (GBLUP model). The deviation of prediction accuracy of between GBLUP and mtGBLUP or GFBLUP was associated with the phenotypic correlation. In summary, the genomic prediction models considering multiple environment phenotypes (GFBLUP and mtGBLUP) demonstrated better prediction accuracy. In addition, we could utilize different genomic prediction strategies according to different availability of multiple environment phenotypes.

A combinatorial strategy to identify various types of QTLs for quantitative traits using extreme phenotype individuals in an F2 population

Theoretical and applied studies demonstrate the difficulty of detecting extremely over-dominant and small-effect genes for quantitative traits via bulked segregant analysis (BSA) in an F2 population. To address this issue, we proposed an integrated strategy for mapping various types of quantitative trait loci (QTLs) for quantitative traits via a combination of BSA and whole-genome sequencing. In this strategy, the numbers of read counts of marker alleles in two extreme pools were used to predict the numbers of read counts of marker genotypes. These observed and predicted numbers were used to construct a new statistic, Gw, for detecting quantitative trait genes (QTGs), and the method was named dQTG-seq1. This method was significantly better than existing BSA methods. If the goal was to identify extremely over-dominant and small-effect genes, another reserved DNA/RNA sample from each extreme phenotype F2 plant was sequenced, and the observed numbers of marker alleles and genotypes were used to calculate Gw to detect QTGs; this method was named dQTG-seq2. In simulated and real rice dataset analyses, dQTG-seq2 could identify many more extremely over-dominant and small-effect genes than BSA and QTL mapping methods. dQTG-seq2 may be extended to other heterogeneous mapping populations. The significance threshold of Gw in this study was determined by permutation experiments. In addition, a handbook for the R software dQTG.seq, which is available at https://cran.r-project.org/web/packages/dQTG.seq/index.html, has been provided in the supplemental materials for the users’ convenience. This study provides a new strategy for identifying all types of QTLs for quantitative traits in an F2 population.

Developing a computer vision system for real-time color measurement – A case study with color characterization of roasted rice

To take advantage of the consistent and automated measurement capability of Computer Vision Systems (CVS), an initial CVS with inclined imaging of the food object was developed to measure its color in a real-time food process. The RGB-L*a*b* color-space transformation model of the CVS was obtained by multiple linear regression. The impact of training data and regularization for high-order regression models was thoroughly evaluated using cross-validation results and mean color error. A case study with roasted rice highlighted the importance of the training data size, the choice of samples in the appropriate color gamut, and the inclusion of representative color samples for a specific target object. The optimal cubic Ridge regression model had the CIEDE2000 mean color errors of 1.73, 1.11, and 1.33 when respectively evaluated with the training dataset and two testing rice datasets. The higher testing accuracy reconfirmed the appropriate training data preparation and demonstrated the benefits of regularization in regression. Based on various performance indices, the adopted model outperformed those reported in the literature. Therefore, the proposed CVS could be reproduced for color characterization of food products in automated and real-time processes. • A CVS was developed for food color measurement in potential real-time food processes that required inclined imaging. • More training data in the appropriate color gamut with representatives of the target object could improve the model accuracy. • A cubic Ridge regression model was optimally obtained for accurate and reliable color-space transformation. • Comparably small CIEDE2000 mean color errors of 1.11 and 1.33 were obtained with two testing rice datasets. • Research results could be reproduced for color characterization of food products in automated and real-time processes.

Diversity of Sodium Transporter HKT1;5 in Genus Oryza

Asian cultivated rice shows allelic variation in sodium transporter, OsHKT1;5, correlating with shoot sodium exclusion (salinity tolerance). These changes map to intra/extracellularly-oriented loops that occur between four transmembrane-P loop-transmembrane (MPM) motifs in OsHKT1;5. HKT1;5 sequences from more recently evolved Oryza species (O. sativa/O. officinalis complex species) contain two expansions that involve two intracellularly oriented loops/helical regions between MPM domains, potentially governing transport characteristics, while more ancestral HKT1;5 sequences have shorter intracellular loops. We compared homology models for homoeologous OcHKT1;5-K and OcHKT1;5-L from halophytic O.coarctata to identify complementary amino acid residues in OcHKT1;5-L that potentially enhance affinity for Na+. Using haplotyping, we showed that Asian cultivated rice accessions only have a fraction of HKT1;5 diversity available in progenitor wild rice species (O. nivara and O. rufipogon). Progenitor HKT1;5 haplotypes can thus be used as novel potential donors for enhancing cultivated rice salinity tolerance. Within Asian rice accessions, 10 non-synonymous HKT1;5 haplotypic groups occur. More HKT1;5 haplotypic diversities occur in cultivated indica gene pool compared to japonica. Predominant Haplotypes 2 and 10 occur in mutually exclusive japonica and indica groups, corresponding to haplotypes in O. sativa salt-sensitive and salt-tolerant landraces, respectively. This distinct haplotype partitioning may have originated in separate ancestral gene pools of indica and japonica, or from different haplotypes selected during domestication. Predominance of specific HKT1;5 haplotypes within the 3 000 rice dataset may relate to eco-physiological fitness in specific geo-climatic and/or edaphic contexts.