Characterization and evaluation of rice blast resistance of Chinese indica hybrid rice parental lines

Hybrid Rice Production: A Worldwide Review of Floral Traits and Breeding Technology, with Special Emphasis on China

Rice is an important diet source for the majority of the world's population, and meeting the growing need for rice requires significant improvements at the production level. Hybrid rice production has been a significant breakthrough in this regard, and the floral traits play a major role in the development of hybrid rice. In grass species, rice has structural units called florets and spikelets and contains different floret organs such as lemma, palea, style length, anther, and stigma exsertion. These floral organs are crucial in enhancing rice production and uplifting rice cultivation at a broader level. Recent advances in breeding techniques also provide knowledge about different floral organs and how they can be improved by using biotechnological techniques for better production of rice. The rice flower holds immense significance and is the primary focal point for researchers working on rice molecular biology. Furthermore, the unique genetics of rice play a significant role in maintaining its floral structure. However, to improve rice varieties further, we need to identify the genomic regions through mapping of QTLs (quantitative trait loci) or by using GWAS (genome-wide association studies) and their validation should be performed by developing user-friendly molecular markers, such as Kompetitive allele-specific PCR (KASP). This review outlines the role of different floral traits and the benefits of using modern biotechnological approaches to improve hybrid rice production. It focuses on how floral traits are interrelated and their possible contribution to hybrid rice production to satisfy future rice demand. We discuss the significance of different floral traits, techniques, and breeding approaches in hybrid rice production. We provide a historical perspective of hybrid rice production and its current status and outline the challenges and opportunities in this field.

Using a Resnet50 with a Kernel Attention Mechanism for Rice Disease Diagnosis

The domestication of animals and the cultivation of crops have been essential to human development throughout history, with the agricultural sector playing a pivotal role. Insufficient nutrition often leads to plant diseases, such as those affecting rice crops, resulting in yield losses of 20-40% of total production. These losses carry significant global economic consequences. Timely disease diagnosis is critical for implementing effective treatments and mitigating financial losses. However, despite technological advancements, rice disease diagnosis primarily depends on manual methods. In this study, we present a novel self-attention network (SANET) based on the ResNet50 architecture, incorporating a kernel attention mechanism for accurate AI-assisted rice disease classification. We employ attention modules to extract contextual dependencies within images, focusing on essential features for disease identification. Using a publicly available rice disease dataset comprising four classes (three disease types and healthy leaves), we conducted cross-validated classification experiments to evaluate our proposed model. The results reveal that the attention-based mechanism effectively guides the convolutional neural network (CNN) in learning valuable features, resulting in accurate image classification and reduced performance variation compared to state-of-the-art methods. Our SANET model achieved a test set accuracy of 98.71%, surpassing that of current leading models. These findings highlight the potential for widespread AI adoption in agricultural disease diagnosis and management, ultimately enhancing efficiency and effectiveness within the sector.

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet

In view of the problem that manual selection of hyperparameters may lead to low performance and large consumption of manpower cost of the convolutional neural network (CNN), this paper proposes a nonlinear convergence factor and weight cooperative self-mapping chaos optimization algorithm (WOACW) to optimize the hyperparameters in the identification and classification model of rice leaf disease images, such as learning rate, training batch size, convolution kernel size and convolution kernel number. Firstly, the opposition-based learning is added to the whale population initialization with improving the diversity of population initialization. Then the algorithm improves the convergence factor, increases the weight coefficient, and calculates the self-mapping chaos. It makes the algorithm have a strong ability to find optimization in the early stage of iteration and fast convergence rate. And disturbance is carried out to avoid falling into local optimal solution in the late stage of iteration. Next, a polynomial mutation operator is introduced to correct the current optimal solution with a small probability, so that a better solution can be obtained in each iteration, thereby enhancing the optimization performance of the multimodal objective function. Finally, eight optimized performance benchmark functions are selected to evaluate the performance of the algorithm, the experiment results show that the proposed WOACW outperforms than 5 other common improved whale optimization algorithms. The WOACW_SimpleNet is used to identify rice leaf diseases (rice blast, bacterial leaf blight, brown spot disease, sheath blight and tungro disease), and the experiment results show that the identification average recognition accuracy rate reaches 99.35%, and the F1-score reaches 99.36%.

Gapless indica rice genome reveals synergistic contributions of active transposable elements and segmental duplications to rice genome evolution

The ultimate goal of genome assembly is a high-accuracy gapless genome. Here, we report a new assembly pipeline that is used to produce a gapless genome for the indica rice cultivar Minghui 63. The resulting 397.71-Mb final assembly is composed of 12 contigs with a contig N50 size of 31.93 Mb. Each chromosome is represented by a single contig and the genomic sequences of all chromosomes are gapless. Quality evaluation of this gapless genome assembly showed that gene regions in our assembly have the highest completeness compared with the other 15 reported high-quality rice genomes. Further comparison with the japonica rice genome revealed that the gapless indica genome assembly contains more transposable elements (TEs) and segmental duplications (SDs), the latter of which produce many duplicated genes that can affect agronomic traits through dose effect or sub-/neo-functionalization. The insertion of TEs can also affect the expression of duplicated genes, which may drive the evolution of these genes. Furthermore, we found the expansion of nucleotide-binding site with leucine-rich repeat disease-resistance genes and cis-zeatin-O-glucosyltransferase growth-related genes in SDs in the gapless indica genome assembly, suggesting that SDs contribute to the adaptive evolution of rice disease resistance and developmental processes. Collectively, our findings suggest that active TEs and SDs synergistically contribute to rice genome evolution.

Prevalence of Ineffective Haplotypes at the Rice Blast Resistance (R) Gene Loci in Chinese Elite Hybrid Rice Varieties Revealed by Sequence-Based Molecular Diagnosis

Multiple haplotypes at the same rice blast R-gene locus share extremely high sequence similarity, which makes the gene diagnostic method using molecular markers less effective in differentiation from one another. The composition and distribution pattern of deployed R genes/haplotypes in elite rice varieties has not been extensively analyzed. In this study, we employed PCR amplification and sequencing approach for the diagnosis of R-gene haplotypes in 54 Chinese elite rice varieties. A varied number of functional and nonfunctional haplotypes of 4 target major R-gene loci, i.e., Pi2/9, Pi5, Pik, and Pib, were deduced by referring to the reference sequences of known R genes. Functional haplotypes accounted for relatively low frequencies for the Pi2/9 (15%) and Pik (9%) loci but for relatively high frequencies for the Pi5 (50%) and Pib (54%) loci. Intriguingly, significant frequencies of 33%, 39%, 46% of non-functional haplotypes at the Pi2/9, Pik, and Pib loci, respectively, with traceable original donors were identified, suggesting that they were most likely unintentionally spread by using undesirable donors in various breeding programs. In the case of Pi5 locus, only a single haplotype, i.e., Pi5 was identified. The reactions of 54 rice varieties to the differential isolates were evaluated, which showed a good correlation to the frequency of cognate avirulence (Avr) genes or haplotypes in the differential isolates. Four R genes, i.e., Pi2, Piz-t, Pi50, and Pikm were found to contribute significantly to the resistance of the elite rice varieties. Other two genes, Pi9 and Pikh, which were not utilized in rice varieties, showed promising values in breeding durable resistance due to their high resistance frequencies to the contemporary rice blast population. The sequence-based molecular diagnosis provided a promising approach for the identification and verification of haplotypes in different R-gene loci and effective R genes valuable for breeding durable rice resistance to rice blast.

Rice Blast Disease Recognition Using a Deep Convolutional Neural Network

Rice disease recognition is crucial in automated rice disease diagnosis systems. At present, deep convolutional neural network (CNN) is generally considered the state-of-the-art solution in image recognition. In this paper, we propose a novel rice blast recognition method based on CNN. A dataset of 2906 positive samples and 2902 negative samples is established for training and testing the CNN model. In addition, we conduct comparative experiments for qualitative and quantitatively analysis in our evaluation of the effectiveness of the proposed method. The evaluation results show that the high-level features extracted by CNN are more discriminative and effective than traditional hand-crafted features including local binary patterns histograms (LBPH) and Haar-WT (Wavelet Transform). Moreover, quantitative evaluation results indicate that CNN with Softmax and CNN with support vector machine (SVM) have similar performances, with higher accuracy, larger area under curve (AUC), and better receiver operating characteristic (ROC) curves than both LBPH plus an SVM as the classifier and Haar-WT plus an SVM as the classifier. Therefore, our CNN model is a top performing method for rice blast disease recognition and can be potentially employed in practical applications.