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The article discusses the virulence mechanisms of Xanthomonas oryzae pv. oryzae, a bacterium causing bacterial leaf blight in rice. It focuses on the type III secretion system (T3SS) and the regulation of hrp genes, essential for bacterial virulence. The study identifies key regulators, such as HrpG and HrpX, and explores the role of xylose in inducing hrp gene expression. Additionally, novel regulators like GamR and XylR are discovered, shedding light on the complex regulatory network controlling bacterial growth and virulence. [Extracted from the article]

Plant‐mediated interactions between herbivores play an important role in regulating the composition of herbivore community. The fall armyworm (FAW), Spodoptera frugiperda, which has become one of the most serious pests on corn in China since it invaded in 2018, has been found feeding rice in the field. However, how FAW interacts with native rice insect pests remains largely unknown. Here, we investigated the interaction between FAW and a resident herbivore, striped stem borer (SSB, Chilo suppressalis) on rice. The infestation of rice leaf sheaths (LSs) by SSB larvae systemically enhanced the level of jasmonic acid (JA), abscisic acid (ABA), and trypsin proteinase inhibitors (TPIs), reduced relative water content (RWC) in leaf blades (LBs), and suppressed the growth of FAW larvae. In contrast, because FAW larvae infested LBs and did not affect defence responses in LSs, they did not influence the performance of SSB larvae. Using different mutants, together with bioassays and chemical analysis, we revealed that SSB‐induced suppression of FAW larvae growth depended on both the SSB‐activated JA pathway and RWC in LBs, whereas the ABA pathway activated by SSB larvae promoted the growth of FAW larvae by impeding water loss. These results provide new insights into mechanisms underlying plant‐mediated interactions between herbivores. Summary Statement: Striped stem borer (SSB) larvae infestation (at rice leaf sheaths [LSs]) suppresses the growth of fall armyworm (FAW) larvae (feeding on rice leaf blades) by systemically activating JA and trypsin proteinase inhibitors, as well as reducing relative water content in rice. In contrast, infestation by FAW larvae had no impact on the performance of SSB larvae because it did not influence defence responses in LSs. [ABSTRACT FROM AUTHOR]

The rice leaf folder, Cnaphalocrocis medinalis (Lepidoptera: Pyralidae), is a major migratory pest in rice agriculture. This pest is characterised by its larvae's ability to fold rice leaves using silk, a behaviour that culminates in the formation of a silken cocoon during the pupal stage. The fibroin light chain (CmFib‐L) gene is crucial for silk production, yet its specific function in C. medinalis has reminded elusive. This study presents a comprehensive analysis of the CmFib‐L gene, revealing its complete open reading frame (ORF) and expression patterns. Notably, the gene is highly expressed in the fifth‐instar larvae and the silk gland, which are critical stages for silk production. Our experiments demonstrate that silencing the CmFib‐L gene leads to a reduction in pupal weight, an extension of the pupal stage and a disorganised silk cocoon. Furthermore, the larval behaviour of leaf folding and spinning is significantly impaired when the expression of CmFib‐L is downregulated. These findings not only show the importance of fibroin light chain in silk production but also reveal a new target gene to regulate and control the behaviour and development of C. medinalis. [ABSTRACT FROM AUTHOR]

The complex structure of the automatic rice detection model results in a delay in identifying diseases and may require higher computational power. To overcome this challenge, we introduced a novel lightweight model called MobileNetV2-Incep-M. MobileNetV2-Incep-M, is designed for rice plant disease classification, aiming to balance efficiency and performance. It combines MobileNetV2 with a single Inception module to create a lightweight architecture. Leveraging transfer learning, the model initializes with pre-trained weights from MobileNetV2 on ImageNet. The Inception module is seamlessly integrated, followed by a max pooling layer for down sampling and parameter reduction. Lastly, a flatten layer and fully connected layer are added for classification purposes. During the training phase we utilized the k-fold cross validation method to reduce the training biasness. The proposed model attained a maximum testing accuracy of 98.75%, a testing loss of 0.0302, and is characterized by the minimal training parameters of 2,502,468, with an average training duration of 464.85 s. We evaluated the proposed model by comparing with five other models, namely InceptionV3, VGG19, MobileNet, MobileNetV2, and DenseNet201. The dataset consists of 5624 images, including Bacterial blight, Leaf Blast, and Brown Spot, and Healthy. The proposed model outperforms the other models, achieving higher accuracy and improved detection of rice plant diseases. Such lightweight model can contribute to the early identification and effective management of rice plant diseases, which can have a substantial impact on agricultural productivity and food security worldwide. [ABSTRACT FROM AUTHOR]

Rice seed-borne diseases caused by the bacterial pathogens Burkholderia glumae and B. plantarii pose a major threat to rice production worldwide. To manage these diseases in a sustainable manner, a biocontrol strategy is crucial. In this study, we showed that B. gladioli NB6 (NB6), a nonpathogenic bacterium, strongly protects rice from infection caused by the above-mentioned pathogens. NB6 was isolated from the indica rice cultivar Nona Bokra seedlings, which possesses genetic resistance to B. glumae. We discovered that cell suspensions of NB6 and its culture filtrate suppressed the disease symptoms caused by B. glumae and B. plantarii in rice seedlings, which indicated that NB6 secretes a plant-protective substance extracellularly. Through purification and mass spectrometry analysis of the culture filtrate, combined with transmission electron microscopy and mutant analysis, the substance was identified as a tailocin and named BglaTNB6. Tailocins are bacteriotoxic multiprotein structures morphologically similar to headless phage tails. BglaTNB6 exhibited antibacterial activity against several Burkholderia species, including B. glumae, B. plantarii, and B. gladioli, suggesting it can prevent pathogen infection. Interestingly, BglaTNB6 greatly contributed only to the biocontrol activity of NB6 cell suspensions against B. plantarii, and not against B. glumae. BglaTNB6 was shown to be encoded by a prophage locus lacking genes for phage head proteins, and a B. gladioli strain with the coded BglaTNB6-like locus equipped with phage head proteins failed to prevent rice seedlings from being infected with B. plantarii. These results suggested that BglaTNB6 may enhance the competitiveness of NB6 against a specific range of bacteria. Our study also highlights the potential of tailocin-producing endophytes for managing crop bacterial diseases. Author summary: Today, there is a strong demand for biopesticides that have a lower environmental impact than chemical pesticides and meet the health and food safety standards expected by consumers. Rice bacterial pathogens, particularly Burkholderia glumae (Bg) and B. plantarii (Bp), are a major concern for rice production worldwide. Here, we describe a novel and promising biocontrol agent, B. gladioli NB6 (hereafter NB6), which strongly protects rice against Bg and Bp infection in the susceptible rice cultivar Nipponbare. NB6 was originally found in an indica rice cultivar, Nona Bokra, which was resistant to Bg. Interestingly, bacterial strains of the Burkholderiaceae family were identified more frequently in Nona Bokra than in Nipponbare. We showed that cell suspensions of NB6 and its culture filtrate suppressed the disease symptoms caused by Bg and Bp in rice seedlings. Using a mutant that secretes proteins in low-abundance, we then identified a tailocin, named BglaTNB6, that exhibited antagonistic activity against several strains of Bg and Bp but not for the other Burkholderia strains. The highly specific antimicrobial activity of tailocins may allow to eliminate pathogens without affecting the beneficial symbiotic bacteria present in agricultural crops. Overall, this study not only reports the discovery of a promising biocontrol agents BglaTNB6 applicable for the management of two rice pathogens, but also suggests that bacterial genetic variants associated with prophage regions may shape bacterial communities through the secretion of tailocins and phages. [ABSTRACT FROM AUTHOR]

The planthopper Nilaparvata muiri is a sister species to N. lugens (Hemiptera: Delphacidae), a notorious insect pest in Asian rice fields. N. muiri and N. lugens have a different host preference despite the similarities in many biological features. To better understand the adaptive evolution of planthoppers, comprehensive genomic information on N. muiri and N. lugens are urgently needed. In this study, we used ultra-low input PacBio HiFi libraries and Hi-C sequencing technologies to assemble a reference genome of a single N. muiri at the chromosomal level. The genome size was determined to be 531.62 Mb with a contig N50 size of 2.47 Mb and scaffold N50 size of 38.37 Mb. Totally, 96.61% assembled sequences were anchored to the 15 pseudo-chromosomes. BUSCO analysis yielded an Insecta completeness score of 98.6%. A total of 22,057 protein-coding genes were annotated, and 168.16 Mb repetitive sequences occupying 31.63% of genome were pinpointed. The assembled genome is valuable for evolutionary and genetic studies of planthoppers, and may provide sights to pest control. [ABSTRACT FROM AUTHOR]

Rice diseases and pests have posed a serious threat to agricultural production in recent years. Particularly, false and missed detections have been caused by the diversity, scale, and complex growth environments of these pests. This study aims to enhance the detection accuracy of rice diseases and pests, in order to facilitate the deployment of detection models on edge devices. An improved YOLOv8-based model was developed to detect rice diseases and pests. Several innovations were also incorporated to optimize the performance of the model. Firstly, partial convolution (PConv) was introduced to design the PCBlock structure and then integrated into the feature extraction module of YOLOv8. As such, the module of lightweight feature extraction known as C2f-PCBlock significantly reduced the number of model parameters, thereby enhancing the detection speed without compromising accuracy. The complexity of the model was then reduced to deploy on the resource- constrained edge devices. Secondly, the semantic information was diluted to cause the feature fusion across non-adjacent layers. A balanced Feature fusion layer was then added to the neck network. feature pyramid networks (FPN) was restructured into balanced feature pyramid (BFP). The preservation of critical features was improved during fusion. Additionally, the embedded Gaussian non-local attention (EGNA) mechanism was incorporated to mitigate the aliasing behavior from the multi- layer feature fusion. The important features were retained to reduce the loss of information, leading to more accurate detections. The loss function of the model was also enhanced to replace the standard IoU-based loss with MPDIOU. The distortions were avoided in the detection bounding boxes, due to the significant variations in sample sizes. MPDIoU was used to reduce these distortions for less computational burden, indicating more efficient training. Extensive experiments were conducted on a rice pest and disease dataset. The results demonstrate that the improved model, named PBM-YOLOv8, outperformed the rest. Specifically, PBM-YOLOV8 was achieved in the precision rate of 6.1, 8.0, 4.2, 1.3, and 1.9 percentage points higher than Fast R-CNN, SSD, YOLOV5n, YOLOv8n, and YOLOv9t, respectively. The recall rates were also significantly improved, with an increase of 7.1, 8.4, 5.6, 2.8, and 3.2 percentage points, respectively. Furthermore, the mean average precision (mAP) values were 5.7, 7.3, 3.8, 1.1, and 1.5 percentage points higher than those of the compared models, respectively. PBM-YOLOv8 was selected as a leading model, in terms of detection accuracy. Moreover, the PBM-YOLOv8 model was highly efficient with only 4.8M parameters, which was far less than the 41.3M of Fast R-CNN and the 15.6 M of SSD. A high processing speed was maintained with an FPS of about 92, in order to balance the accuracy with computational efficiency. The effectiveness of the improved model was further validated. PBM-YOLOv8 was deployed on the RK3588 embedded board. The deployment showed an accelerated detection speed of 71.4 frames per second, with an average accuracy of 95.1%. This performance can fully meet the demands of practical applications, thus enabling real-time and accurate detection of rice pests and diseases. The multi-thread optimization was adopted in the deployment. The parallel acceleration of multi-core NPU was also realized for the model inference and CPU for post-processing. The model has further enhanced the efficiency and feasibility for real-world use. [ABSTRACT FROM AUTHOR]

This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet's lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions. [ABSTRACT FROM AUTHOR]

This article discusses a study on creating broad-spectrum resistance to bacterial blight in rice using targeted insertion. The researchers used CRISPR/Cas9 gene editing to insert effector binding elements (EBEs) into rice genes, resulting in resistance against diverse strains of Xanthomonas oryzae pv. oryzae (Xoo), the bacteria that causes bacterial blight. The study found that incorporating multiple EBEs into rice genes increased resistance to a greater number of Xoo strains. The researchers also used genome sequencing to identify novel EBEs and demonstrated the effectiveness of sequencing-guided EBE deployment in enhancing resistance. The study suggests that this programmable immunity system could help counter resistance breakdown in rice varieties. [Extracted from the article]

Understanding the genetic basis of adaptive evolution following habitat expansion can have important implications for pest management. The pink rice borer (PRB), Sesamia inferens (Walker), is a destructive pest of rice that was historically restricted to regions south of 34° N latitude in China. However, with changes in global climate and farming practices, the distribution of this moth has progressively expanded, encompassing most regions in North China. Here, 3 highly differentiated subpopulations were discovered using high‐quality single‐nucleotide polymorphism and structural variant datasets across China, corresponding to northern, southern China regions, and the Yunnan‐Guizhou Plateau, with significant patterns of isolation by geographic and environmental distances. Our estimates of evolutionary history indicate asymmetric migration with varying population sizes across the 3 subpopulations. Selective sweep analyses estimated strong selection at insect cuticle glycine‐rich cuticular protein genes which are associated with enhanced desiccation adaptability in the northern group, and at the histone‐lysine‐N‐methyltransferase gene associated with range expansion and local adaptation in the Shandong population. Our findings have significant implications for the development of effective strategies to control this pest. [ABSTRACT FROM AUTHOR]

BACKGROUND: With the rapid development of deep learning, the recognition of rice disease images using deep neural networks has become a hot research topic. However, most previous studies only focus on the modification of deep learning models, while lacking research to systematically and scientifically explore the impact of different data sizes on the image recognition task for rice diseases. In this study, a functional model was developed to predict the relationship between the size of dataset and the accuracy rate of model recognition. RESULTS: Training VGG16 deep learning models with different quantities of images of rice blast‐diseased leaves and healthy rice leaves, it was found that the test accuracy of the resulting models could be well fitted with an exponential model (A = 0.9965 − e(−0.0603×I50−1.6693)). Experimental results showed that with an increase of image quantity, the recognition accuracy of deep learning models would show a rapid increase at first. Yet when the image quantity increases beyond a certain threshold, the accuracy of image classification would not improve much, and the marginal benefit would be reduced. This trend remained similar when the composition of the dataset was changed, no matter whether (i) the disease class was changed, (ii) the number of classes was increased or (iii) the image data were augmented. CONCLUSIONS: This study provided a scientific basis for the impact of data size on the accuracy of rice disease image recognition, and may also serve as a reference for researchers for database construction. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The plant pathogenic bacterium Burkholderia glumae causes bacterial panicle blight (BPB) in rice-growing areas worldwide. It has been widely accepted that an acyl-homoserine lactone (AHL)-type quorum sensing (QS) system encoded by tofI and tofR genes (TofIR QS) is a key regulatory mechanism underlying the bacterial pathogenesis of B. glumae. In addition, qsmR, which encodes an IclR-family regulatory protein, has been considered an important part of TofIR QS. However, the present study with three strains of B. glumae representing different pathogenic strains revealed that this currently accepted paradigm should be modified. We characterized the regulatory function of TofIR QS and qsmR in three different strains of B. glumae, 336gr-1 (virulent), 411gr-6 (hypervirulent) and 257sh-1 (avirulent). In 336gr-1, both TofIR QS and qsmR were critical for the pathogenesis, being consistent with previous studies. However, in the hypervirulent strain 411gr-6, TofIR QS only partially contributes to the virulence, whereas qsmR was critical for pathogenesis like in 336gr-1. Furthermore, we found that a single nucleotide polymorphism causing T50K substitution in the qsmR coding sequence was the cause of the non-pathogenic trait of the naturally avirulent strain 257sh-1. Subsequent analyses of gene expression and transcriptome revealed that TofIR QS is partially controlled by qsmR at the transcriptional level in both virulent strains. Further genetic tests of additional B. glumae strains showed that 11 out of 20 virulent strains retained the ability to produce toxoflavin even after removing the tofI/tofM/tofR QS gene cluster like 411gr-6. In contrast, all the virulent strains tested lost the ability to produce toxoflavin almost completely upon deletion of the qsmR gene. Taking these results together, qsmR, rather than TofIR QS, is a master regulator that determines the pathogenic trait of B. glumae thus a more appropriate pathogen target for successful management of BPB. Author summary: Bacterial cell-to-cell communication, called quorum-sensing, systems mediated by homoserine lactone (HSL)-type signaling molecules play pivotal roles in the virulence-related functions of diverse plant and animal pathogenic Gram-negative bacteria. Burkholderia glumae is the chief causal agent for bacterial panicle blight, a devastating rice disease worldwide. In this pathogen, the HSL-type quorum-sensing system dependent on the tofI and tofR genes had long been thought to be the core regulatory/signaling system essential for virulence. However, we discovered in this study that a highly virulent strain of B. glumae, 411gr-6, retained its virulence even after the tofI/tofR-dependent quorum-sensing function was disabled. We also found that more than a half of the natural strains of this pathogen species tested in this study also exhibited their virulence function in the absence of the tofI/tofR-dependent quorum-sensing system. Then we characterized the qsmR gene as an essential regulatory/signaling element for the bacterial pathogenesis regardless of the genetic variations among the strains of B. glumae. This study indicates that the qsmR gene, rather than the tofI/tofR-dependent quorum-sensing system, should be considered as an essential master regulatory/signaling factor for the virulence of B. glumae and, thus, a promising target for suppressing bacterial panicle blight. [ABSTRACT FROM AUTHOR]

Sulfur dioxide (SO2) fumigation was studied in laboratory to determine its potential as an alternative treatment for postharvest control of stored product insects, confused flour beetle, Tribolium confusum Jacquelin du Val (Coleoptera: Tenebrionidae), and rice weevil, Sitophilus oryzae (L.) (Coleoptera: Curculionidae). Three-hour fumigations with 0.1%–2.0% SO2 were conducted against eggs, immature stages, and adults of the 2 insects at 20 °C. Effective control of both insects was achieved. However, there were considerable variations between the 2 insects and among different life stages. Confused flour beetle was more susceptible to SO2 fumigation than rice weevil. Complete control of adults and all life stages of confused flour beetle was achieved in 3-h fumigations with 0.5% and 2.0% SO2, respectively. For rice weevil, 3-h fumigation with 1.5% SO2 resulted in 96.5% adult mortality and the fumigation with 2.0% SO2 resulted in 99.27% mortality of adults and 87.5% mortality of immature stages. Three-hour fumigations with 1% SO2 resulted in <5% egg survival to adults. The study demonstrated high efficacy of SO2 fumigation against the insects and suggested that SO2 fumigation has good potential for postharvest pest control on stored products. [ABSTRACT FROM AUTHOR]

AbstractSudden outbreaks of agricultural pests are increasing in India due to the growing prominence of environmental issues like global warming, the occurrence of weather extremes, and hydrological extremes. Climate change impacts the developmental rates and population dynamics of agricultural pests. As rice is a staple food widely used in India, it often suffers intensive damage by various pests, leading to a considerable decrease in crop yield. One of the most common pests in rice fields in India and neighboring countries is the rice thrips (Stenchaetothrips biformis). However, the degree of pest infestation of rice crops under climate change scenarios is poorly known. Therefore, this study aimed to understand the potential geographical distribution of the rice thrips in India for future scenarios using the MaxEnt modeling framework. Future projections from global climate models (GCMs), such as HadGEM3-GC31 and MPI-ESMI-2, are used to predict the most affected regions in 2050 under three greenhouse gas emission scenarios: SSP1-2.6, SSP3-4.5, and SSP5-8.5. The results from the MaxEnt indicate that the southern and northeastern parts of India will be highly affected by rice thrips in 2050. The affected number of districts are found to be 256, 330 and 313 under SSP1-2.6, SSP3-4.5, and SSP5-8.5 scenarios, respectively. It has been observed that the maximum temperature is the most influential climatic parameter for the geographical distribution of rice thrips, followed by the annual mean and minimum temperatures. It can be concluded from these results that the elevated temperature enhances the growth and distribution of rice thrips. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content is an important indicator of photosynthetic capacity and health status and plays a key role in monitoring plant growth. At present, research on chlorophyll content monitoring has mainly focused on establishing a relationship between vegetation index and chlorophyll content, which was composed of several hyperspectral wavelengths, resulting in the loss of effective information contained in certain wavelengths strongly associated with disease. Therefore, it was very important to select spectral wavelengths to reveal the hyperspectral response of chlorophyll content in diseased leaves. Fifty groups of rice leaves infected with bacterial leaf blight were studied, and hyperspectral images and soil plant analysis development (SPAD) values of disease‐stressed rice leaves were collected every 5 days from the first day after inoculation. A method for predicting the SPAD value of diseased rice leaves using random forest (RF) combined with a back‐propagation optimized neural network (BPNN) was proposed. The bird swarm algorithm (BSA) and the immune algorithm (IA) were used to solve the problems of information loss, local minimization, and slow convergence of the BPNN. The BPNN, BSA–BPNN, IA–BPNN, and BSA–IA–BPNN prediction models for the SPAD value of diseased rice leaves are established. The results show that the BSA–IA–BPNN model with characteristic wavelengths selected by RF achieved the best prediction effect, and the prediction sets R2, root‐mean‐square error, and relative error are 0.97%, 4.34%, and 3.33% respectively. The shortest prediction modeling time is 1.54 s, and the model exhibits the best stability. This study provides allegorical support and a theoretical basis for achieving rapid, nondestructive detection of the SPAD value of diseased rice leaves, provides an effective reference for estimating the SPAD value of diseased rice leaves in the field, and is of great importance for rice disease monitoring. [ABSTRACT FROM AUTHOR]

The Nilaparvata muiri (Hemiptera: Delphacidae) is a sibling species of a destructive rice insect pest, the brown planthopper (BPH), Nilaparvata lugens. Here, we generated a high-quality chromosome-level genome assembly of N. muiri using a combination of the PacBio HiFi sequencing, Illumina short-read sequencing and Hi-C scaffolding technologies. The genome assembly (524.9 Mb) is anchored to 15 pseudochromosomes, with a scaffold N50 of 43.3 Mb and 99.1% BUSCO completeness. It contains 188.1 Mb repeat sequences and 13204 protein-coding genes. As a closely related species within the same genus as the significant pest, N. lugens, the chromosome-level genome assembly of N. muiri will provide important support for the better analysis of pathogenicity mechanisms of N. lugens based on comparative genomics. [ABSTRACT FROM AUTHOR]

Small non-coding RNAs (sRNAs) are pivotal post-transcriptional regulatory factors influencing biological activity. Studies on the rice bacterial blight pathogen Xanthomonas oryzae pathovar oryzae strain PXO99A, previously identified a virulence-associated sRNA, trans3287. A mutant strain lacking this sRNA, named SK01, resulted in markedly diminished virulence towards rice. This study aims to further elucidate the underlying bacterial virulent function of trans3287. The expression of trans3287 was quantified in virulence-inducing and standard nutritional conditions to clarify its production mechanism. The detection of virulence-associated genes revealed that trans3287 regulated the synthesis processes of extracellular polysaccharides, lipopolysaccharides, and the type III secretion system. Moreover, bioinformatics prediction and quantitative PCR indicated a potential direct target of trans3287, PXO_03470, encoding cardiolipin synthase. A dual-plasmid system fusing with GFP tag and protein immunoblotting confirmed that sRNA trans3287 negatively regulated PXO_03470. Bacterial biofilms demonstrated trans3287 regulated the disruption of biofilm integrity through cardiolipin synthase. This study provides preliminary insights into the mechanistic underpinnings of the role of sRNA trans3287 in mediating bacterial virulence through cardiolipin synthase. [ABSTRACT FROM AUTHOR]

Rice is one of the most important food crops in the world, and its yield restricts global food security. However, various diseases and pests of rice pose a great threat to food security. Among them, bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of the most serious bacterial diseases affecting rice globally, creating an increasingly urgent need for research in breeding resistant varieties. Phytohormones are widely involved in disease resistance, such as auxin, abscisic acid (ABA), ethylene (ET), jasmonic acid (JA), and salicylic acid (SA). In recent years, breakthroughs have been made in the analysis of their regulatory mechanism in BLB resistance in rice. In this review, a series of achievements of phytohormones in rice BLB resistance in recent years were summarized, the genes involved and their signaling pathways were reviewed, and a breeding strategy combining the phytohormones regulation network with modern breeding techniques was proposed, with the intention of applying this strategy to molecular breeding work and playing a reference role for how to further improve rice resistance. [ABSTRACT FROM AUTHOR]

Numerous farmers worldwide are impacted by diseases connected to rice leaves that frequently endanger the sustainability of the rice industry. Diseases that affect the leaves of rice plants severely limit their ability to produce rice, and they are typically brought on by bacteria, viruses, or fungi. This paper proposes an innovative classification scheme for rice leaf diseases based on Federated Learning (FL). Here, FL framework comprises two entities, namely nodes and servers. Every node does initial local training using local data. Moreover, produced local model is then updated on server. Model aggregation is done at the server since several nodes update their local models and send them to it. The nodes download the global model that server has generated as a result. The nodes update their training using transferred global model and local model. The following series of actions are taken in the training model. The input image is mainly obtained from a database and pre-processed with a Kalman filter to eliminate noise. Then, numerous operations for data augmentation are applied. In addition, feature extraction is done and generated features are used in LeNet for rice leaf diseases classification. LeNet is trained using the Spotted Hyena Archimedes Optimizer (SHAO). The developed method shows better precision of 91.3%, recall of 92.2%, f-measure of 91.7%, loss function of 3.3%, Mean Square Error (MSE) of 7.3%, and Root MSE of 27.1%. [ABSTRACT FROM AUTHOR]

ObjectivesMethodsResultsConclusionIron (Fe) accumulation and resultant oxidative stress play a significant role in the neuronal death observed in Parkinson’s disease (PD). Brown rice (BR) possesses antioxidant properties able to reduce cellular oxidative damage. Thus, we hypothesized that BR may ameliorate Fe-induced parkinsonism due to oxidative stress.Two – to three-day-old male flies were concurrently exposed to Fe (ferrous sulphate, 1 mM) and interventions, divided into eight groups: control; Fe; BR; white rice (WR); L-dopa (1 mM); Fe (1 mM) + BR; Fe (1 mM) + WR; and Fe (1 mM) + L-dopa (1 mM). The flies were exposed for 15 days to their respective diets, and their behavior, relevant biomarkers, and the expression of related genes were evaluated.Chronic exposure to Fe caused cognitive and locomotor deficits by increasing Fe levels (p = 0.027) in flies’ heads, as well as heightened aggression and grooming episodes (p < 0.001). The elevated iron levels induced changes consistent with oxidative stress, evidenced by increased MDA levels (p < 0.001), and reduced activity of catalase (p < 0.001) and glutathione peroxidase (GPx) (p < 0.001), along with decreased dopamine levels (p < 0.001). Additionally, there was dysregulation in the mRNA expression of malvolio, ferritin, Nrf2, DJ-1, GPx, and catalase (p < 0.05). BR prevented the Fe-induced effects (Fe + BR group) even more effectively than L-Dopa (p < 0.001).The findings indicate that BR has the potential to mitigate Fe-induced ROS-mediated damage in a Drosophila model of PD-like disease by modulating key players in the Nrf2 signaling pathway. [ABSTRACT FROM AUTHOR]

NAC transcription factors (TFs) are pivotal in plant immunity against diverse pathogens. Here, we report the functional and regulatory network of MNAC3, a novel NAC TF, in rice immunity. MNAC3, a transcriptional activator, negatively modulates rice immunity against blast and bacterial leaf blight diseases and pathogen‐associated molecular pattern (PAMP)‐triggered immune responses. MNAC3 binds to a CACG cis‐element and activates the transcription of immune‐negative target genes OsINO80, OsJAZ10, and OsJAZ11. The negative function of MNAC3 in rice immunity depends on its transcription of downstream genes such as OsINO80 and OsJAZ10. MNAC3 interacts with immunity‐related OsPP2C41 (a protein phosphatase), ONAC066 (a NAC TF), and OsDjA6 (a DnaJ chaperone). ONAC066 and OsPP2C41 attenuate MNAC3 transcriptional activity, while OsDjA6 promotes it. Phosphorylation of MNAC3 at S163 is critical for its negative functions in rice immunity. OsPP2C41, which plays positive roles in rice blast resistance and chitin‐triggered immune responses, dephosphorylates MNAC3, suppressing its transcriptional activity on the target genes OsINO80, OsJAZ10, and OsJAZ11 and promoting the translocation of MNAC3 from nucleus to cytoplasm. These results establish a MNAC3‐centered regulatory network in which OsPP2C41 dephosphorylates MNAC3, attenuating its transcriptional activity on downstream immune‐negative target genes in rice. Together, these findings deepen our understanding of molecular mechanisms in rice immunity and offer a novel strategy for genetic improvement of rice disease resistance. [ABSTRACT FROM AUTHOR]

Rapid and accurate diagnosis of rice diseases can prevent large-scale outbreaks and reduce pesticide overuse, thereby ensuring rice yield and quality. Existing research typically focuses on a limited number of rice diseases, which makes these studies less applicable to the diverse range of diseases currently affecting rice. Consequently, these studies fail to meet the detection needs of agricultural workers. Additionally, the lack of discussion regarding advanced detection algorithms in current research makes it difficult to determine the optimal application solution. To address these limitations, this study constructs a multi-class rice disease dataset comprising eleven rice diseases and one healthy leaf class. The resulting model is more widely applicable to a variety of diseases. Additionally, we evaluated advanced detection networks and found that DenseNet emerged as the best-performing model with an accuracy of 95.7%, precision of 95.3%, recall of 94.8%, F1 score of 95.0%, and a parameter count of only 6.97 M. Considering the current interest in transfer learning, this study introduced pre-trained weights from the large-scale, multi-class ImageNet dataset into the experiments. Among the tested models, RegNet achieved the best comprehensive performance, with an accuracy of 96.8%, precision of 96.2%, recall of 95.9%, F1 score of 96.0%, and a parameter count of only 3.91 M. Based on the transfer learning-based RegNet model, we developed a rice disease identification app that provides a simple and efficient diagnosis of rice diseases. [ABSTRACT FROM AUTHOR]

Simple Summary: Rice breeding programs have included traditional varieties and wild rice species as sources of resistance to planthoppers and leafhoppers, which are major insect pests of rice in Asia. However, herbivores can adapt to resistance (known as virulence adaptation), sometimes in as little as 2 years. Virulence adaptation is a major obstacle for deploying resistant varieties. This review assesses knowledge of the rates, consequences, and mechanisms of virulence adaptation and on possible strategies to delay adaptation in rice landscapes. The review indicates that virulence is a complex of responses to minor and major resistance factors. Of particular interest are adaptations to major rice resistance genes that are likely governed by corresponding herbivore virulence genes. Evidence suggests that pre-adapted individuals (called forerunners) with these virulence genes occur in planthopper populations at relatively low frequencies but can rapidly build up numbers in response to planted resistant rice. However, gradual shifts in virulence may also occur due to intra- and intergenerational improvements in the herbivores' abilities to detoxify plant defense toxins, or to confound defense response networks. These may involve gradual changes in the expression of genes involved in successful herbivore attacks and may be associated with the herbivores or their endosymbionts. Several research gaps remain. In particular, there is a need for increased attention to practical, ecologically based deployment strategies that delay adaptation. In recent decades, research on developing and deploying resistant rice has accelerated due to the availability of modern molecular tools and, in particular, advances in marker-assisted selection. However, progress in understanding virulence adaptation has been relatively slow. This review tracks patterns in virulence adaptation to resistance genes (particularly Bph1, bph2, Bph3, and bph4) and examines the nature of virulence based on selection experiments, responses by virulent populations to differential rice varieties (i.e., varieties with different resistance genes), and breeding experiments that interpret the genetic mechanisms underlying adaptation. The review proposes that varietal resistance is best regarded as a combination of minor and major resistance traits against which planthoppers develop partial or complete virulence through heritable improvements that are reversable or through evolutionary adaptation, respectively. Agronomic practices, deployment patterns, and herbivore population pressures determine the rates of adaptation, and there is growing evidence that pesticide detoxification mechanisms can accelerate virulence adaptation. Research to delay adaptation has mainly focused on gene pyramiding (i.e., including ≥ two major genes in a variety) and multilines (i.e., including ≥ two resistant varieties in a field or landscape); however, these strategies have not been adequately tested and, if not managed properly, could inadvertently accelerate adaptation compared to sequential deployment. Several research gaps remain and considerable improvements in research methods are required to better understand and manage virulence adaptation. [ABSTRACT FROM AUTHOR]

Simple Summary: The rice leaf-roller Cnaphalocrocis medinalis is a migratory pest of rice. Monitoring its population is important in integrated pest management systems. Trapping them with a sex pheromone or plant odor has been used in population monitoring. We studied the physiological status of adults trapped by sex pheromones and floral odors. In the immigrant group, the number of males caught in the floral trap was greater than those caught by the sex pheromone trap. The volumes of testes in adults caught using the above two trapping methods were similar. In the local breeding group, the number of males caught by sex pheromone trapping was greater than that by floral trapping. The volume of testes was smaller in males caught in the floral odor trap compared to the pheromone trap. In the emigrant group, the adult olfactory response to the sex pheromone and floral odor was low. The number of eggs laid by the females in the local breeding group was greater in those caught in the sweep net in comparison with those caught in the floral odor trap. The rice leaf-roller Cnaphalocrocis medinalis is an important migratory pest of rice. We conducted a study to determine the physiological status of adults trapped by a sex pheromone and floral odor. In the immigrant group, the number of males trapped by the floral odor was greater than the number caught by sex pheromone trapping. The volume of testes was similar in the above two trapping methods but was smaller than in the sweep net method. The ovary developmental grade, mating rate, and number of matings of females caught in floral odor trap were higher than in those caught in the sweep net. In the local breeding group, the number of males trapped by sex pheromones was greater than the number trapped by the floral odor. The volume of testes was smaller in the floral odor trap compared to the pheromone trap group, with the largest in the sweep net group. The ovarian developmental grade, mating rate, and number of matings of females were significantly higher in the floral odor trap group than in the sweep net group. In the emigrant group, the adult olfactory response to the sex pheromone and floral odor was low. The volume of testes was larger in the sweep net group compared to the moths caught by floral odor trapping. The number of eggs laid by female immigrants trapped by the floral odor and sweep net was similar, while the number in the local breeding group was greater in moths caught with the sweep net in comparison with those caught by the floral odor trap. The difference in egg hatchability between the two trapping methods in both immigrants and local breedings was not significant. [ABSTRACT FROM AUTHOR]

• Brown Planthopper is a serious threat to rice crop, posing a significant risk to food security. • To combat the damage, molecular breeding stands as an efficient approach by utilizing resistance genes. • Accordingly, a sophisticated marker assisted breeding technique was employed with SSRs and SNPs to introduce BPH genes from M229 and 10–3 donors into susceptible Telangana Sona. • Five backcross lines (BC 1 (a)−14, BC 1 (a)−19, BC 1 (b)−2, BC 1 (b)−4, BC 1 (b)−5) from both the crosses were identified as potential materials, displaying grain characteristics similar to Telangana Sona. Brown Planthopper (BPH) is one of the most destructive insect pests of rice that threatens food security. Molecular breeding through the use of resistance genes per se would be an economic and efficient way to combat the damage caused by BPH. Telangana Sona (TS) is an elite, popular and a high yielding rice variety susceptible to the infestation of BPH causing severe yield losses. In the current investigation, a marker-assisted backcross breeding approach using gene-specific Simple Sequence Repeats (SSRs) and designed Single Nucleotide Polymorphisms (SNPs) was employed to incorporate BPH resistance genes from the donor lines, M229 and 10–3 into TS. Foreground selection using gene-specific SSRs pinned down one plant (BC 1 (a)-19) from TS//TS/M229 cross with qBph-3–1 and Bph33(t) genes/QTLs, while, six plants of TS//TS/10–3 cross had Bph33(t) gene. Validation of BC 1 F 1 plants with designed SNPs altogether identified three (BC 1 (a)-14, BC 1 (a)-18, BC 1 (a)-19) from TS//TS/M229 and three lines (BC 1 (b)-2, BC 1 (b)-4, BC 1 (b)-5) from TS//TS/10–3 cross positive for snpOS00915, snpOS00922 and snpOS00923. All these lines were also found to be phenotypically resistant to BPH as screened using a modified mass tiller screening method. Altogether, foreground selection coupled with stringent phenotypic selection and agro-morphological evaluation led to the identification of two backcross lines (BC 1 (a)-14 and BC 1 (a)-19) from TS//TS/M229 and three (BC 1 (b)-2, BC 1 (b)-4, BC 1 (b)-5) from TS//TS/10–3 that had grain type characteristics similar to TS that can be potentially used as breeding material for further backcrossing and advancing for near isogenic lines development. The study demonstrates the potential efficacy of marker-assisted selection deployed with both SSRs and SNPs that can be employed in breeding for BPH-resistant varieties. [ABSTRACT FROM AUTHOR]

Asian rice gall midge is one of the important pests of rice, which attacks the crop from nursery to the end of the tillering stage. Managing this pest through plant resistance is the most viable and economical. Two hundred‐two rice genotypes were phenotyped against Asian rice gall midge, Orseolia oryzae (Wood‐Mason) and the mechanism of resistance in terms of antixenosis and antibiosis was studied. Antixenosis mechanism for adult settlement and egg laying indicated that the susceptible genotypes were preferred more than the resistant genotypes in a free choice test. First instar maggots were found and did not continue their growth on resistant genotypes further. However, in susceptible genotypes, they molted successfully and emerged as adults. No choice tests revealed that the emergence of adults in susceptible genotypes took less time than in resistant genotypes. Higher adult sex ratio was found in susceptible genotypes. Estimation of biochemical components in rice shoot apices of selected genotypes revealed that higher levels of total phenols, wax content, total flavonoids and total free amino acids were present in the resistant genotypes. Still, the number of total sugars, reducing sugars and total protein contents were significantly higher in the susceptible genotypes. Hence, the resistance mechanism in rice gall midge was displayed as a combination of antixenosis and antibiosis mechanism. The output of the study would be helpful in breeding for rice varieties resistant to Asian rice gall midge. [ABSTRACT FROM AUTHOR]

SUMMARY: Bacterial leaf streak (BLS), caused by Xanthomonas oryzae pv. oryzicola (Xoc), is a major bacterial disease in rice. Transcription activator‐like effectors (TALEs) from Xanthomonas can induce host susceptibility (S) genes and facilitate infection. However, knowledge of the function of Xoc TALEs in promoting bacterial virulence is limited. In this study, we demonstrated the importance of Tal10a for the full virulence of Xoc. Through computational prediction and gene expression analysis, we identified the hexokinase gene OsHXK5 as a host target of Tal10a. Tal10a directly binds to the gene promoter region and activates the expression of OsHXK5. CRISPR/Cas9‐mediated gene editing in the effector binding element (EBE) of OsHXK5 significantly increases rice resistance to Xoc, while OsHXK5 overexpression enhances the susceptibility of rice plants and impairs rice defense responses. Moreover, simultaneous editing of the promoters of OsSULTR3;6 and OsHXK5 confers robust resistance to Xoc in rice. Taken together, our findings highlight the role of Tal10a in targeting OsHXK5 to promote infection and suggest that OsHXK5 represents a potential target for engineering rice resistance to Xoc. [ABSTRACT FROM AUTHOR]

In Asian countries, rice is the major agricultural product. Hence, the initial identification of plant disorder via the IoT (Internet of Things) offers to ignore the rice from critical disorders. To increase crop production, the measurements should be considered to completely destroy the rice plant disorders by an effective system. This paper proposed an Intelligent IoT-aided deep learning model for detecting rice blast fungal along with a hybrid heuristic algorithm. The proposed work encompasses with multiple stages that are explained as follows. At first, the required paddy images are gathered from online data resources. Next, the pre-processing of the collected images is made by adaptive mean filtering and contrast enhancement. Further, the adaptive thresholding and morphological operation are adopted for leaf segmentation purposes, where the threshold value is tuned by a Fitness-based Billiards-inspired Rat Swarm Optimizer (FBRSO). Consequently, from the segmented image, the Region of Interest (ROI) is cropped. Finally, the cropped ROI is subjected to the Optimized MobileNetv2 and Multiscale Residual Attention Network (OMMRAN), where it includes MobileNetV2 and Multiscale Residual Attention Network, in which some of the hyperparameters are tuned by FBRSO approach. The performance is validated and compared with other existing approaches for detecting rice diseases effectively. [ABSTRACT FROM AUTHOR]

• Spider community composition changed during the rice growing season. • The rice field development generally decreased the number of horizontal web-building spiders and captured detritivorous prey. • Species identity and species traits of spider as well as environmental factors affect prey capture by web-building spiders. Understanding the effects of microhabitat changes on arthropod predator communities and their prey in agroecosystems is essential for field management and biocontrol. Few studies have investigated the trait composition of web-building spider communities in rice ecosystems. Here, we examined how temporal changes during the rice field development affect the abundance and traits of orb-web spiders, and how these effects consequently influence captured prey number and prey composition in irrigated rice ecosystems in southern Thailand. We used structural equation models to evaluate direct and indirect, spider-mediated effects of rice field development on captured prey numbers in each different guild. We found that the number of horizontal web-building spiders decreased during the rice field development, whereas there was no significant change in number of vertical web-building spiders. The number of captured detritivorous insects was positively related to the numbers of horizontal and vertical web-building spiders, while phytophagous insects and others were positively related only to the numbers of vertical web-building spiders. Moreover, the prey number captured by vertical web-building spiders seems to be indirectly increased through the decreasing number of horizontal web-building spiders in the late rice season. A fourth-corner analysis showed that spider species identity, spider traits (web type, web height and web diameter), vegetation height, and water level generally influenced the prey captured by web-building spiders. Horizontal web-building spider species with lower web placement during the flooding phase captured high numbers of detritus-feeding insects, while vertical web-building spider species with higher web placement captured high numbers of rice pests, predators and others. Our results suggest that the field development acted as an environmental factor that determined the species identity and traits of web-building spider communities. The findings of this study can help to predict the ecosystem services provided by the web-building spider community in rice ecosystems. [ABSTRACT FROM AUTHOR]

Rice yellow hairy caterpillar Psalis pennatula Fabricius (Lepidoptera: Lymantriidae) is an emerging pest of rice and an increased incidence is expected in different rice-growing areas of Assam in the coming years. The caterpillar is a voracious leaf feeder and can consume the whole leaves within a day. Keeping this in view, an attempt was made to evaluate the bio-efficacy of three important native isolates of entomopathogenic fungal strains viz. Beauveria bassiana (KR855715), Cordyceps javanica (OM321438) and Isaria fumosorosea (MH414514) against P. pennatula larvae under laboratory conditions during the Sali season of rice. All three EPF strains at different conidial dilutions (1x103, 1x105,1x107, 1x108 and1x109 conidia/ml) were found to be pathogenic to the 3rd instar larvae of P. pennatula. Among the tested fungal strains, the strain Isaria fumosorosea (MH414514) showed the highest mortality (100%) at 1x107 conidia/ml and Cordyceps javanica (OM321438) showed the lowest mortality (51%) at 1x107conidia/ml at 10th day of post-treatment. The tested fungal isolates could play a vital role as microbial biopesticides in suppressing the P. pennatula population in Assam and could be incorporated into IPM strategies. [ABSTRACT FROM AUTHOR]

Pursuing enhanced nutritional value in bakery products through technological advancements and new recipes is a promising facet of the food industry. This study focuses on incorporating rice and buckwheat brans, additional raw materials rich in biologically active substances, into bakery products. Utilizing a second-order rotatable plan, optimal ratios were determined—5% rice bran and 10% buckwheat bran. The application of these brans influenced dough and bread quality, reducing sugar content by 5% in dry form and 29% in the fermented brew, potentially aiding in diabetes prevention and cholesterol control. Introducing brans, especially in fermented brew, positively impacted microbiological stability, reducing the risk of mold and potato disease. The developed bread technology using rice and buckwheat brans in fermented brew significantly increased nutritional value, satisfying adult daily protein needs by 31.2%, fats by 15%, and dietary fibers by 18.4%. This innovative approach ensures a sufficient intake of essential vitamins and minerals, showcasing a promising avenue for creating healthier and more nutritious bakery products. [ABSTRACT FROM AUTHOR]

BACKGROUND: The brown planthopper (BPH), Nilaparvata lugens, is one of the most destructive pests of rice. Owing to the rapid adaptation of BPH to many pesticides and resistant varieties, identifying putative gene targets for developing RNA interference (RNAi)‐based pest management strategies has received much attention for this pest. The glucoprotein papilin is the most abundant component in the basement membranes of many organisms, and its function is closely linked to development. RESULTS: In this study, we identified a papilin homologous gene in BPH (NlPpn). Quantitative Real‐time PCR analysis showed that the transcript of NlPpn was highly accumulated in the egg stage. RNAi of NlPpn in newly emerged BPH females caused nonhatching phenotypes of their eggs, which may be a consequence of the maldevelopment of their embryos. Moreover, the transcriptomic analysis identified 583 differentially expressed genes between eggs from the dsGFP‐ and dsNlPpn‐treated insects. Among them, the 'structural constituent of cuticle' cluster ranked first among the top 15 enriched GO terms. Consistently, ultrastructural analysis unveiled that dsNlPpn‐treated eggs displayed a discrete and distorted serosal endocuticle lamellar structure. Furthermore, the hatchability of BPH eggs was also successfully reduced by the topical application of NlPpn‐dsRNA‐layered double hydroxide nanosheets onto the adults. CONCLUSION: Our findings demonstrate that NlPpn is essential to maintaining the regular structure of the serosal cuticle and the embryonic development in BPH, indicating NlPpn could be a potential target for pest control during the egg stage. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: The use of unmanned aerial vehicles (UAVs) for the application of plant protection products (PPPs) in paddy fields is becoming increasingly prevalent worldwide. Despite its growing usage, UAV spraying for rice pest control faces practical challenges, including limited canopy penetration, uneven deposition, and significant spray drift. This study investigated the impact of two tank‐mix adjuvants, Wonderful Rosin (Adjuvant‐1) and Tiandun (Adjuvant‐2), at six volume concentrations, on the spray liquid's physicochemical properties, spray drift, plant deposition, and the biological efficacy of rice insecticides using a quadrotor UAV sprayer. RESULTS: The physicochemical characteristics of the spray liquid influenced spray performance and biological efficacy. Incorporating Adjuvant‐1 and Adjuvant‐2 led to a decrease in surface tension and contact angle while increasing the viscosity of the spray solution. These alterations in surface tension and viscosity contributed to an optimized droplet size distribution, reduced spray drift, enhanced deposition uniformity and penetration, and improved control efficacy against the rice planthopper in UAV applications. The highest control efficacy was observed at a concentration of 0.5%, showing an improvement of 35.12% (Adjuvant‐1) and 20.23% (Adjuvant‐2) over applications without tank‐mix adjuvant 7 days after treatment. CONCLUSION: The judicious selection of tank‐mix adjuvants for UAV PPP applications can significantly enhance spray performance and biological efficacy in controlling rice insects. This study's findings offer valuable insights for integrating tank‐mix adjuvants into UAV spraying applications. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient ( R c v 2 ) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields. [ABSTRACT FROM AUTHOR]

[Background] Intercropping is considered an effective approach to defending rice disease. [Objectives/Methods] This study aimed to explore the resistance mechanism of rice intraspecific intercropping by investigating soil metabolites and their regulation on the rhizosphere soil microbial community using metabolomic and microbiome analyses. [Results] The results showed that the panicle blast disease occurrence of the resistant variety Shanyou63 (SY63) and the susceptible variety Huangkenuo (HKN) were both decreased in the intercropping compared to monoculture. Notably, HKN in the intercropping system exhibited significantly decreased disease incidence and increased disease resistance-related enzyme protease activity. KEGG annotation from soil metabolomics analysis revealed that phenylalanine metabolic pathway, phenylalanine, tyrosine, and tryptophan biosynthesis pathway, and fructose and mannose metabolic pathway were the key pathways related to rice disease resistance. Soil microbiome analysis indicated that the bacterial genera Nocardioides, Marmoricola, Luedemannella, and Desulfomonile were significantly enriched in HKN after intercropping, while SY63 experienced a substantial accumulation of Ruminiclostridium and Cellulomonas. Omics-based correlation analysis highlighted that the community assembly of Cellulomonas and Desulfomonile significantly affected the content of the metabolites D-sorbitol, D-mannitol, quinic acid, which further proved that quinic acid had a significantly inhibitory effect on the mycelium growth of Magnaporthe oryzae, and these three metabolites had a significant blast control effect. The optimal rice blast-control efficiency on HKN was 51.72%, and Lijiangxintuanheigu (LTH) was 64.57%. [Conclusions] These findings provide a theoretical basis for rice varieties intercropping and sustainable rice production, emphasizing the novelty of the study in elucidating the underlying mechanisms of intercropping-mediated disease resistance. [ABSTRACT FROM AUTHOR]

In warm and humid regions, the productivity of sorghum is significantly limited by the fungal hemibiotrophic pathogen Colletotrichum sublineola, the causal agent of anthracnose, a problematic disease of sorghum (Sorghum bicolor (L.) Moench) that can result in grain and biomass yield losses of up to 50%. Despite available genomic resources of both the host and fungal pathogen, the molecular basis of sorghum-C. sublineola interactions are poorly understood. By employing a dual-RNA sequencing approach, the molecular crosstalk between sorghum and C. sublineola can be elucidated. In this study, we examined the transcriptomes of four resistant sorghum accessions from the sorghum association panel (SAP) at varying time points post-infection with C. sublineola. Approximately 0.3% and 93% of the reads mapped to the genomes of C. sublineola and Sorghum bicolor, respectively. Expression profiling of in vitro versus in planta C. sublineola at 1-, 3-, and 5-days post-infection (dpi) indicated that genes encoding secreted candidate effectors, carbohydrate-active enzymes (CAZymes), and membrane transporters increased in expression during the transition from the biotrophic to the necrotrophic phase (3 dpi). The hallmark of the pathogen-associated molecular pattern (PAMP)-triggered immunity in sorghum includes the production of reactive oxygen species (ROS) and phytoalexins. The majority of effector candidates secreted by C. sublineola were predicted to be localized in the host apoplast, where they could interfere with the PAMP-triggered immunity response, specifically in the host ROS signaling pathway. The genes encoding critical molecular factors influencing pathogenicity identified in this study are a useful resource for subsequent genetic experiments aimed at validating their contributions to pathogen virulence. This comprehensive study not only provides a better understanding of the biology of C. sublineola but also supports the long-term goal of developing resistant sorghum cultivars. [ABSTRACT FROM AUTHOR]

Tomato disease image recognition plays a crucial role in agricultural production. Today, whilemachine vision methods based on deep learning have achieved some success indiseaserecognition, theystill faceseveralchallenges. These include issues such as imbalanced datasets, unclear disease features, small inter-class differences, and large intra-class variations. To address these challenges, this paper proposes a method for classifying and recognizing tomato leaf diseases based on machine vision. First, to enhance the disease feature details in images, a piecewise linear transformation method is used for image enhancement, and oversampling is employed to expand the dataset, compensating for the imbalanced dataset. Next, this paper introduces a convolutional block with a dual attention mechanismcalled DAC Block, which is used to construct a lightweight model named LDAMNet. The DAC Block innovatively uses Hybrid Channel Attention (HCA) and Coordinate Attention (CSA) to process channel information and spatial information of input images respectively, enhancing the model's feature extraction capabilities. Additionally, this paper proposes a Robust Cross-Entropy (RCE) loss function that is robust tonoisylabels, aimedat reducing the impact of noisy labels on the LDAM Net model during training. Experimental results show that this method achieves an average recognition accuracy of 98.71% on the tomato disease dataset, effectively retaining disease information in images and capturing disease areas. Furthermore, the method also demonstrates strong recognition capabilities on rice crop disease datasets, indicating good generalization performance and the ability to function effectively in disease recognition across different crops. The research findings of this paper provide new ideas and methods for the field of crop disease recognition. However, future research needs to further optimize the model's structure and computational efficiency, and validate its application effects in more practical scenarios. [ABSTRACT FROM AUTHOR]

Fermented products contain probiotic organisms that have beneficial and therapeutic effects on the gastrointestinal tract. The main objective of the study is to isolate probiotic bacteria from fermented sour traditional rice water and to evaluate their probiotic activity. The microbes were isolated from the fermented rice water and the characterization of the organisms was determined using MALDI-TOF MS. The morphological analysis, biochemical test, and carbohydrate fermentation test were done for further characterization. The colonization and therapeutic properties of organisms were evaluated by performing in vitro simulation studies. The results indicated that the isolated gram-positive organisms Pediococcus pentosaecus and Lactococcus lactis from traditional fermented sour rice water possessed desirable in vitro probiotic properties. Consuming fermented sour rice water enriches intestinal flora with beneficial bacteria and enzymes. Fermented rice water improves gut microbiome health, immune system function, and is also used to treat chronic conditions. [ABSTRACT FROM AUTHOR]

The present experiment was conducted from June to November, 2017 at Crop Research Center, Chirodi, Sardar Vallabhbhai Patel University of Agriculture & Technology, Meerut (U.P.), India to assess the efficacy of six fungicides-propiconazole, strobin, flutolanil, copper oxychloride, azoxystrobin, flusilazol, and two bioagents viz., Trichoderma harzianum and Pseudomonas fluorescens-against false smut of rice under natural epiphytotic field conditions. Once panicle emergence reached 50%, each fungicide and bioagent was sprayed once. The findings of present investigation revealed that spray of propiconazole @ 0.1% reduced the false smut significantly and resulted in the lowest percentage of infected tillers (7.48%) and smutted grains (4.98%) along with maximum yield parameters like number of tillers (294), 10-ears weight (30.10 g), 1000-grain weight (24.50 g) and yield (4903 kg ha-1); it was followed by the spray application of either azoxystrobin @ 0.07% or strobin @ 0.06% with above respective parameters. Apart from the fungicides applied, the application of Trichoderma harzianum also reduced the false smut and increased the yield components significantly over the control plots. However, the lowest 10-ear weight (22.80 g), 1000-grain weight (17.50 g), and yield (3100 kg ha-1) along with the highest percentage of infected tillers (23.93%) and smutted grains (21.47%) were recorded in unprotected plots. Therefore, growers can be advised to use a foliar spray of one of three fungicides-propiconazole @ 0.1%, azoxystrobin @ 0.07%, or strobin @ 0.06%-at 50% panicle emergence stage to fungicidal control of false smut of rice. [ABSTRACT FROM AUTHOR]

Scirpophaga incertulas Walker (Lepidoptera: Crambidae, yellow stem borer, YSB) is a monophagous insect pest that causes significant yield loss in rice (Oryza staiva L.). Semiochemical based pest management is being sought as an alternate to chemical pesticides to reduce pesticide footprints. We hypothesized differential release of volatiles from host rice and two companion non-host weeds, Echinochloa colona and Echinochloa crus-galli could be responsible for oviposition and biology of YSB and these chemicals could be used for YSB management. Number of eggs laid, and number of larvae hatched were significantly higher in rice plant as compared to weeds. YSB could only form dead hearts in rice plants. YSB significantly preferred host-plant volatiles compared to the non-host plants both in choice and no-choice tests in an Y-tube olfactometer. 2-Hexenal, hexanal, 2,4-hexadienal, benzaldehyde, nonanal, methyl salicylate and decanal were found in the leaf volatolomes of both the host and non-host plants in HS-SPME-GC-MS (Headspace-Solid phase micro extraction-Gas chromatography-Mass spectrometer). Pentene-3-one, 2-pentyl furan, 2,4-heptadienal, 2-octenal, 2-octenol and menthol were present only in the non-host plants. Fourteen rice unique compounds were also detected. The built-in PCA (Principal Component Analysis) and PLS-DA (Partial least squares-discriminant analysis) analysis in the MS-DIAL tool showed that the volatiles emitted from TN1 formed a cluster distinct from Echinochloa spp. and 2-octenal was identified as a unique compound. Olfactometer bioassays using synthetic compounds showed that rice unique compounds, like xylene, hexanal served as attractants whereas non-host unique compounds, like 2-pentylfuran, 2-octenal acted as repellent. The results indicate that the rice unique compounds xylene, hexanal along with other volatile compounds could be responsible for higher preference of YSB towards rice plants. Similarly, the non-host unique compounds 2-pentylfuran, 2-octenal could possibly be responsible for lower preference and defence against YSB. These compounds could be utilised for devising traps for YSB monitoring and management. [ABSTRACT FROM AUTHOR]

Brown planthopper (BPH), Nilaparvata lugens (Stal) is a monophagous and one of most devastating pest of rice. Application of insecticides is inevitable to control the infestation of BPH on rice crops. However, the indiscriminate use of various insecticides in rice is leading to pest resurgence. Several factors have been connected to the resurgence of brown planthoppers, especially the reduction in natural enemies and changes in plant nutrition after insecticide application. Hence, in the present study, we studied the resurgence of BPH post application of different insecticides in rice and assessed the impact of insecticides on natural enemy population during Rabi 2022-23. Among nine insecticides tested, two insecticides viz., deltamethrin 11 EC @ 15 g a.i/ha (44.70%) and imidacloprid 17.8 SL @ 25 g a.i/ha (23.01%) significantly increased the population of N. lugens over untreated check and consequently resulted in higher BPH resurgence. A greater reduction in predatory mirid bugs and spiders was observed in the deltamethrin and imidacloprid treated groups compared to the untreated check. In conclusion, we found that deltamethrin and imidacloprid induce the BPH resurgence in rice compared to other tested insecticides and the observed resurgence was partly due to the reduction of natural enemies. [ABSTRACT FROM AUTHOR]

A study published in the journal iMeta examines the role of beneficial microbes in protecting rice plants from fungal pathogens. The researchers found that certain microbes in the rice plants' microbiome can directly suppress fungal infections. They created a simplified community of three beneficial bacteria, which was shown to enhance rice resistance against fungal pathogens in both lab and field conditions. The study suggests that these beneficial microbes could be used as biocontrol agents to improve rice productivity and manage plant diseases in a cost-effective way. [Extracted from the article]

RD6, the most popular glutinous rice in Thailand, is high in quality but susceptible to blast and bacterial blight disease. It was thus improved for disease resistance through marker-assisted backcross selection (MAS). The objective of this study was to evaluate the performance of improved near isogenic lines. Eight RD6 rice near isogenic lines (NILs) derived from MAS were selected for evaluation with RD6, a standard susceptible check variety, as well as recurrent parent for a total of nine genotypes. The experiment was conducted during the wet season under six environments at three locations, Khon Kaen, Nong Khai, and Roi Et, which was repeated at two years from 2019 to 2020. Nine genotypes, including eight RD6 rice near isogenic lines (NILs) selected from two in-tandem breeding programs and the standard check variety RD6, were evaluated to select the high-performance new improved lines. The first group, including four NILs G1–G4, was gene pyramiding of blast and BB resistance genes, and the second group, including another four NILs G5–G8, was gene pyramiding of blast resistance and salt tolerance genes. Field disease screening was observed for all environments. Two disease occurrences, blast (leaf blast) and bacterial blight, were found during the rainy season of all environments. The NILs containing blast resistance genes were excellent in gene expression. On the other hand, the improved lines containing the xa5 gene were not highly resistant under the severe stress of bacterial blight (Nong Khai 2020). Notwithstanding, G2 was greater among the NILs for yield maintenance than the other genotypes. The agronomic traits of most NILs were the same as RD6. Interestingly, the traits of G2 were different in plant type from RD6, specifically photosensitivity and plant height. Promising rice RD6 NILs with high yield stability, good agronomic traits, and disease resistance were identified in the genotypes G1, G2, and G7. The high yield stability G1 and G7 are recommended for widespread use in rain-fed areas. The G2 is specifically recommended for use in the bacterial blight (BB) disease prone areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rice bakanae disease (RBD) is a typical seed-borne fungal disease caused by Fusarium fujikuroi. Prochloraz is a sterol demethylation inhibitor, which is among the most important classes of active ingredients for the management of RBD. In 2022, the total resistance frequency of F. fujikuroi to prochloraz in Zhejiang Province was 62.67%. The fitness of the prochloraz-resistant population was lower than that of the susceptible population, but its pathogenicity was slightly stronger. The S312T and F511S double mutations of Ffcyp51b were detected in the resistant isolates. Loop-mediated isothermal amplification (LAMP) technology based on S312T was established to rapidly determine prochloraz resistance in F. fujikuroi. LAMP primer mismatch design was performed based on the cyp51b gene, and 100–300 bp sequences containing a mutation at codon 312 were amplified. In a 25 µL reaction tube, 1 pg/µL DNA of F. fujikuroi could be detected. The detection limit for the frequency of prochloraz resistance was 0.498% using this method. We performed LAMP detection on rice seedlings inoculated with prochloraz-sensitive and -resistant isolates and treated them with prochloraz. Prochloraz demonstrated good control in rice seedlings. A chromogenic reaction was observed in seedlings treated with prochloraz-resistant isolates, and the results were verified using electrophoresis. It has been demonstrated that LAMP technology based on the S312T genotype can quickly and specifically detect prochloraz-resistant isolates in rice seedlings. [ABSTRACT FROM AUTHOR]

Sheath rot and dirty panicle are some of the major diseases of rice in Thailand. The diseases are traditionally considered to be caused by the pathogen Sarocladium oryzae and damage and lower both the quantity and quality of rice grain. In this study, 32 fungal isolates collected from the central and northeastern regions of Thailand were analysed phylogenetically using three molecular markers (ITS, D1/D2 of 28S rDNA and ACT) and physiological races were determined on 10 differential rice cultivars. We found that S. oryzae is not the only causal agent of sheath rot in Thailand, but S. attenuatum was also found. Despite having similar morphological features, the phylogenetic analysis recognised 11 of 32 isolates as S. attenuatum and the remaining isolates as S. oryzae. This is the first report of S. attenuatum causing sheath rot of rice in Thailand in addition to S. oryzae. Evaluation of physiological races revealed high pathogenic diversity of the two species. Thus, 16 and 11 physiological races were recorded from 21 isolates of S. oryzae and 11 isolates of S. attenuatum, respectively. These results indicate that both S. oryzae and S. attenuatum are the causal agents of rice sheath rot and dirty panicle in Thailand and that they are pathologically diverse. [ABSTRACT FROM AUTHOR]

Summary: The clade III subfamily of OsSWEETs includes transmembrane proteins necessary for susceptibility to bacterial blight (BB). These genes are targeted by the specific transcription activator‐like effector (TALE) of Xanthomonas oryzae pv. oryzae and mediate sucrose efflux for bacterial proliferation. However, the mechanism through which OsSWEETs regulate rice immunity has not been fully elucidated. Here, we demonstrated that the cytosolic carboxyl terminus of OsSWEET11a/Xa13 is required for complementing susceptibility to PXO99 in IRBB13 (xa13/xa13). Interestingly, the C‐terminus of ZmXa13, the maize homologue of OsSWEET11a/Xa13, could perfectly substitute for the C‐terminus of OsSWEET11a/Xa13. Furthermore, OsSWEET11a/Xa13 interacted with the high‐mobility group B1 (OsHMGB1) protein and the small heat shock‐like protein OsHsp20L through the same regions in the C‐terminus. Consistent with the physical interactions, knockdown or knockout of either OsHMGB1 or OsHsp20L caused an enhanced PXO99‐resistant phenotype similar to that of OsSWEET11a/OsXa13. Surprisingly, the plants in which OsHMGB1 or OsHsp20L was repressed developed increased resistance to PXO86, PXO61 and YN24, which carry TALEs targeting OsSWEET14/Xa41 or OsSWEET11a/Xa13. Additionally, OsHsp20L can interact with all six members of clade III OsSWEETs, whereas OsHMGB1 can interact with five other members in addition to OsSWEET12. Overall, we revealed that OsHMGB1 and OsHsp20L mediate conserved BB susceptibility by interacting with clade III OsSWEETs, which are candidates for breeding broad‐spectrum disease‐resistant rice. [ABSTRACT FROM AUTHOR]

Simple Summary: We exposed brown planthoppers (BPHs) Nilaparvata lugens to the entomopathogenic fungus Cordyceps javanica PSUC002 and observed morpho-histological changes from 0 to 120 h post-inoculation (pi). At 12 h pi, we observed the first filamentous fungi on the external integument and the first fungal conidia penetrated host planthopper cells at 24 h pi. In contrast, we observed the initial degeneration of BPHs at 6 h pi, especially in the integument and adipose tissue. We also found the degeneration of integument and adipose tissue at 12 h pi, and their completed necrosis became clear at 96 h pi. This study illustrated the process of C. javanica infection in BPHs and demonstrated that fungal agents can be used to control the BPHs in an integrated pest management (IPM) program. The brown planthopper (BPH), Nilaparvata lugens (Stål, 1854), is a pest of rice plants worldwide. Cordyceps javanica is a destructive entomopathogenic fungus known to attack leafhoppers or BPHs specifically. Live adult BPH samples were inoculated with isolated C. javanica PSUC002, and their interaction was morpho-histologically examined from 0 to 120 h post-inoculation (pi). We observed that the mortality of BPH continuously increased until 120 h pi (Day 5). Tissue alterations in the host were examined after infection using morphological and histological methods, including the Grocott Methenamine Silver stain test (GMS). Filamentous fungi were first found on the external integument at 12 h pi, and fungal conidia attached to the integument at 24 h pi. However, the initial degeneration of BPHs was identified by histology at 6 h pi especially in the integument and adipose tissue. We identified the degeneration and loss of integument and adipose tissue of infected BPHs at 12 h pi, and their necrosis was completed at 96 h pi. The enzymatic index of the sampled fungi (chitinase and protease) peaked at 7 days of incubation. This study demonstrated that C. javanica PSUC002 is useful to control the BPHs as an eco-friendly practice and will possibly be applied in agriculture. [ABSTRACT FROM AUTHOR]

Temporal change in vegetation complexity and field management may interactively affect the structure of predator–prey networks in agroecosystems and consequently alter the biocontrol potential of predators. There is a limited number of studies that have addressed these questions for generalist predator–prey networks.We investigated how vegetation complexity during crop development and management type (conventional vs. organic) affect the trophic networks of orb‐weaving spiders and their prey in rainfed lowland rice ecosystems in southern Thailand. Specifically, we investigated orb‐weaving spiders and prey composition, prey selectivity and network structure.Overall, orb‐weaving spiders captured mostly detritus‐associated Diptera, aquatic‐associated Ephemeroptera and Hemiptera pests. The increasing vegetation complexity during rice development significantly restructured the network of orb‐weaving spiders and prey, while field management had only a marginal effect. The increased vegetation complexity during rice development led to an increased number of weak trophic interactions in comparison to few but strong interactions in simple vegetation.Our results indicate that increasing the number of spider species per prey taxon (prey vulnerability) in late rice season may enhance their top‐down control of prey including rice pests. This study also highlights that the network complexity and the stability of rice ecosystems increased during the rice growing season as the vegetation became more complex.Future practices could look for a way to support the densities of detritus and aquatic insects and to artificially increase habitat complexity during the early stage of rice development to improve the biocontrol services provided by the orb‐weaving spiders. [ABSTRACT FROM AUTHOR]

The identification and classification of pests in rice field are the prerequisites of early warning systems for pest dis-asters. Among these pests, the rice planthoppers cause the most serious damage. However, the existing deep learning models for rice planthopper recognition are characterized by large size and numerous parameters, which makes them unsuitable for the deployment on embedded devices with limited computational resources. To address this issue, a lightweight rice planthopper recognition model based on YOLOv5 is proposed in this paper. In the model, a lightweight convolutional network named GhostNet is employed as the backbone to reduce the operational parameters. Additionally, a convolutional attention module (CBAM) is integrated into the backbone network to effectively enhance the transmission of deep information, so as to improve the model's ability of recognizing rice planthopper images. The original CIoU loss function is replaced by the SIoU loss function to expedite model convergence. Experimental results demonstrate that the modified model achieves the mAP@0.5 as 82.8%, with the parameter count of 3.12x106 and the model size of 7.2MB. Compared to the original model, it is a reduction of 46.7% in size and 43.3% in parameters, with a minor accuracy loss of 0.1%. Clearly, the improved model can achieve lightweight characteristics and robust performance, and thus it provides a theoretical and practical foundation for early warning systems against rice planthopper infestations in rice fields. [ABSTRACT FROM AUTHOR]