Rice crops are frequently threatened by pests such as rice planthoppers (Nilaparvata lugens, Sogatella furcifera, and Laodelphax striatellus) and leafhoppers (Cicadellidae), which cause significant yield losses. Accurate identification of both pest developmental stages and their natural predators is crucial for effective pest control and maintaining ecological balance. However, conventional field surveys are often subjective, inefficient, and lack traceability. To overcome these limitations, this study proposed RiceInsectID, a two-stage cascaded detection method designed to identify and count tiny rice pests and their natural predators from white flat plate images captured by head-worn AR glasses. The method recognizes 25 insect classes, including 17 instars of rice planthoppers, 2 instars of leafhoppers, 4 spider species (Araneae), as well as Miridae and rove beetles (Staphylinidae Latreille). At the first coarse-grained detection stage, 16 visually similar classes are consolidated into 6 broader categories and detected using an enhanced YOLOv6 model. To improve small object detection and address class imbalance, the full-region overlapping sliding slices and target pasting (FOSTP) algorithm was applied, increasing the mean average precision at a 50% IoU threshold (mAP₅₀) by 35.46% over the baseline YOLOv6. Feature extraction and fusion were further improved by incorporating an efficient channel attention path aggregation feature pyramid network (ECA-PAFPN) and adaptive structure feature fusion (ASFF) modules, while the balanced classification mosaic (BCM) enhanced detection of minority classes. With test-time augmentation (TTA), mAP₅₀ improved by an additional 2.06%, reaching 84.71%. At the second fine-grained classification stage, each of the six broad classes from the first stage is further classified using individual ResNet50 models. Online data augmentation and transfer learning were employed to significantly enhance generalization. Compared with the baseline YOLOv6, the two-stage cascaded method improved recall by 4.06%, precision by 3.79%, and the F1-score by 3.92%. Overall, RiceInsectID achieved 82.85% recall, 80.62% precision, and an F1-score of 81.72%, demonstrating an efficient and practical solution for monitoring tiny rice pests and their natural predators in paddy fields. This study provides valuable insights for ecosystem monitoring and supporting sustainable pest management in rice agriculture.

The classification of parboiled rice into types can be optimized through the use of machine learning (ML) algorithms, resulting in greater speed and accuracy in data processing. The objectives of this study were: (i) to investigate the spectral behavior of different types of parboiled rice (Types 1-5 and Off-type); (ii) to identify the most effective ML algorithm for classifying parboiled rice types; (iii) to determine the best kernel configuration and preprocessing methods for spectral data; and (iv) to recommend a protocol for implementing this technique in the rice storage industry. Samples were selected based on the maximum defect limits tolerated for each type, according to the Technical Rice Regulation. Spectral data were acquired using a spectroradiometer in the range of 350-2500 nm and subsequently processed with different methods, including baseline correction, standard normal variate, multiplicative scattering correction, combinations of these techniques with Savitzky-Golay smoothing, and the application of the first derivative of Savitzky-Golay smoothing. The data were analyzed using six different ML algorithms: Artificial Neural Network, Decision Tree, Logistic Regression, REPTree, Random Forest, and Support Vector Machine. Rice types were treated as output variables, while spectral features served as input variables. Logistic Regression and Support Vector Machine algorithms showed the best classification performance, with accuracy rates above 97%, F-scores around 0.98, and Kappa values exceeding 0.97. Spectral preprocessing did not yield substantial improvements and incurred high computational costs; therefore, using raw data was a viable and efficient alternative. For practical implementation in the rice storage industry, we recommend acquiring a VNIR-SWIR (visible near-infrared and shortwave infrared) hyperspectral sensor (350-2500 nm) and developing a classification model based on the Support Vector Machine algorithm with a linear kernel trained on representative local samples. Additionally, we recommend implementing an automated real-time classification system, a representative sample collection protocol, and detailed reporting for inventory and logistics optimization.

Rice production, essential for global food security, is increasingly impacted by climate variability and genetic improvements. However, limited research has systematically quantified the individual contributions of climate change and genetic advancements to rice yield trends, particularly in high-latitude regions such as Harbin city, Heilongjiang Province, China. This study addresses this gap by using the AquaCrop model to partition the effects of climate change and genetic enhancements on rice yields over recent decades. The objectives were to evaluate the relative influences of climate and genotype on yield trends, assess irrigation efficiency under continuous flooding (CF) and alternate wetting and drying (AWD), and identify optimal transplanting dates for yield and water productivity. Four years of paddy field data were used to calibrate and validate AquaCrop for three rice varieties (V₁, V₂, and V₃) under CF and AWD irrigation. Historical climate data were sourced for simulations. Key findings indicated that climate change accounts for 60%‒70% of yield improvements, while genotype contributes 30%‒40%. AWD achieved grain yields within 1% of CF, while improving water productivity by up to 7% in later (V₂ and V₃) varieties and with delayed transplanting dates. Additionally, 15 May was identified as the optimal transplanting date, yielding up to 7.53 t/hm² under CF with biomass reaching 18.35 t/hm². These findings highlight strategies for sustainable rice production in water-scarce regions and emphasize the role of genotype development in climate adaptation.

Phosphorus (P) deficiency is a major constraint in rice production, causing significant reductions in growth and yield. While P deficiency typically decreases chlorophyll content in many plant species, our previous studies revealed an unexpected increase in chlorophyll content in P-deficient rice seedlings. Here, we investigated this phenomenon in KDML105 rice under various P regimes and analyzed the physiological mechanisms involved. We found that P-deficient rice seedlings significantly increased chlorophyll a, chlorophyll b, and carotenoid contents in young leaves while reducing photosystem II quantum yield and enhancing non-photochemical quenching. This response was specific to P deficiency and was not observed under other stress conditions such as salinity or copper toxicity, which induced oxidative stress. Time-course experiments revealed that increased chlorophyll accumulation was an early adaptive response that occurred primarily in young leaves, while older leaves eventually developed chlorosis under prolonged P deficiency. The increased chlorophyll content may be attributed to reduced leaf width and altered leaf morphology under P-limited conditions. Furthermore, using custom hyperspectral imaging analysis coupled with machine learning classification, we successfully differentiated P status in rice leaves with 98.96% accuracy in older leaves. This study demonstrates that enhanced chlorophyll accumulation is a characteristic early response to P deficiency in rice, rather than a typical general stress response observed in other conditions. Our findings highlight the limitations of relying solely on chlorophyll-based indices as indicators of plant health in precision agriculture, especially regarding phosphorus (P) nutrition management. This underscores the need for a more comprehensive approach and lays the groundwork for developing advanced remote sensing technologies aimed at accurately assessing P status in rice cultivation.

Rice cultivation contributes up to 12% of global anthropogenic methane (CH₄) emissions, making it a significant climate concern. With rice demand projected to double by 2050, achieving the required 2.4% annual genetic gain must be balanced with emission reduction. This review synthesizes recent progress in three key areas: (1) mitigation strategies such as alternate wetting and drying and direct-seeded rice, which can reduce CH₄ emissions by 30%-40%; (2) identification of physiological and molecular traits, such as short duration, high harvest index, improved nitrogen use efficiency, optimized root architecture, and stress tolerance with reduced greenhouse gas (GHG) footprints; and (3) the potential of genomics-assisted breeding and high-throughput phenotyping to accelerate the development of climate-resilient rice varieties with lower CH₄ emissions. Specifically, we highlight how the synergistic integration of high-throughput phenotyping, genomic selection, and marker-assisted breeding can substantially improve the efficiency and precision of breeding programs targeting the development of climate-resilient rice varieties with reduced CH₄ emissions. This is exemplified through successful case studies utilizing multi-omics approaches, including the development of Green Super Rice varieties (GSR 2 and GSR 8), which have demonstrated up to a 37% reduction in GHG emissions. Crucially, we propose a stratified trait profile for low-GHG rice development and provide guidelines and metrics for integrating these traits into mainstream breeding pipelines. We conclude by proposing a strategic framework integrating carbon-efficient breeding, climate-adapted agronomy, and policy support, which is essential for scaling low-GHG rice systems globally.

Phytic acid (PA), or myo-inositol 1,2,3,4,5,6-hexakisphosphate, is the main storage form of phosphorus (P) in seeds, accounting for 65% to 85% of their total P content. The negative charge of PA attracts metal cations, forming insoluble salts called phytates. These phytates, contain six negatively charged ions, can bind divalent cations such as Fe²⁺, Zn²⁺, Mg²⁺, and Ca²⁺, preventing their absorption in monogastric animals. To overcome P deficiency in non-ruminants, phytase is usually given as a supplement, which then results in excess P excretion, leading to environmental problems such as eutrophication. Improved fertilizer management, food processing techniques, and the development of low-PA crops through plant breeding are envisioned as effective ways to improve P-utilization and lessen the environmental impact while minimizing the effect of PA. A better understanding of the molecular and physiological basis of PA biosynthesis, grain PA distribution, the effects of genetic and environmental factors on PA accumulation, and methods to increase micronutrient bioavailability by lowering the effects of PA is essential for developing low-PA crops.

In recent years, traditional rice landraces have gained increasing attention among consumers, scientists, and nutritionists because of their nutritional and therapeutic value. The diverse rice gene pool of the Indian subcontinent is bestowed with indigenous rice types augmented with nutrients and phytochemicals. Landraces high in resistant starch and dietary fiber contribute to gut health and help prevent gastrointestinal disorders, whereas those with high-quality protein contents, such as glutelin and lysine, all-trans retinoic acid, as well as iron and zinc contents (even in polished rice), play a vital role in the alleviation of malnutrition and hidden hunger. Metabolomic studies have revealed the presence of novel bioactive molecules, including tocols (e.g., gamma-tocotrienol and alpha-tocopherol), phytosterols (e.g., campestrol, beta-sitosterol, and stigmasterol), phenolic acids (e.g., 2-methoxy-4-vinylphenol, 4-vinylphenol, 3,5-di-tert-butylphenol, 2,4-di-tert-butylphenol, ionol, and 2,6-di-tert-butylphenol), flavonoids [e.g., flavonolignans tricin 4′-O-(threo-β-guaiacylglyceryl) ether and tricin 4′-O-(erythro-β-guaiacylglyceryl) ether], anthocyanins (e.g., delphinidin and cyanidin), carotenoids (e.g., 7,7′,8,8′-tetrahydrolycopene and 1-hydroxylycopene), diterpenoids (e.g., sugiol), vitamin D3 (a secosteroid), and bioactive vitamin D (e.g., calcitriol). These bioactive phytochemicals endow Indian rice landraces, rich in antioxidants, with antiphlogistic, antineoplastic, cardiac risk preventive, antiviral, and antitubercular activities, confirming their use in traditional Indian medicine. Furthermore, Indian landraces with a low glycemic index may benefit the Asian Indian phenotype, which is characterized by clinical anomalies such as insulin resistance, dyslipidemia (reduced high-density lipoprotein levels), and high dietary glycemic load. Therefore, the conservation of India’s traditional rice varieties is vital for both sustainable agriculture and improving global health.

Accurate evaluation of disease levels in wild rice germplasm and identification of disease resistance are critical for developing rice varieties resistant to blast disease. However, existing evaluation methods face limitations that hinder progress in breeding. To address these challenges, we proposed an AI-powered method for evaluating blast disease levels and identifying resistance in wild rice. A lightweight segmentation model for diseased leaves and lesions was developed, incorporating an improved federated learning approach to enhance robustness and adaptability. Based on the segmentation results and resistance identification technical specifications, wild rice materials were evaluated into 10 disease levels (L0 to L9), further enabling disease-resistance identification through multiple replicates of the same materials. The method was successfully implemented on augmented reality glasses for real-time, first-person evaluation. Additionally, high-speed scanners and edge computing devices were integrated to enable continuous, precise, and dynamic evaluation. Experimental results demonstrate the outstanding performance of the proposed method, achieving effective segmentation of diseased leaves and lesions with only 0.22 M parameters and 5.3 G floating-point operations per second (FLOPs), with a mean average precision (mAP@0.5) of 96.3%. The accuracy of disease level evaluation and disease-resistance identification reached 99.7%, with a practical test accuracy of 99.0%, successfully identifying three highly resistant wild rice materials. This method provides strong technical support for efficiently identifying wild rice materials resistant to blast disease and advancing resistance breeding efforts.

Assessing the resilience of rice varieties against bioterrorism agents is critical to safeguarding food security. This study evaluated Food and Drug Administration-approved and recognized as safe metallic oxide nanoparticles (NPs) of zinc oxide (ZnO) and magnesium oxide (MgO) as protective strategies to reduce susceptibility in imported rice varieties to a biothreat model, Escherichia coli. Two types of rice (brown and white) from four countries (USA, Mexico, India, and Thailand) were treated with 60 mg/L NPs or their ionic forms and sterilized before inoculation. The treatments were analyzed for nutritional profiles, heavy metal content, and pathogen susceptibility. Rice organic compositions were characterized by Fourier transform infrared spectroscopy, and metal were contents quantified using inductively coupled plasma optical emission spectroscopy. Pathogenic response was monitored using ultraviolet mass spectrophotometry. The findings revealed that nutrient-rich varieties like brown rice from Mexico displayed reduced susceptibility to E. coli compared with white rice from India, which showed the highest susceptibility. NP fortification demonstrated significant antimicrobial efficacy, particularly with ZnO and MgO NPs, which were more effective than their ionic counterparts in inhibiting bacterial growth. Results showed that ZnO and MgO NP treatments reduced E. coli growth by 72% and 68%, respectively, compared with untreated controls. Brown rice from Mexican treated with MgO NPs exhibited the lowest optical density at 600 nm (OD₆₀₀ 0.01), indicating significantly enhanced resistance to bacterial proliferation. This research underscores the potential of nano-fortification not only to improve pathogen resilience in rice but also to maintain its nutritional integrity. This study provides a foundational framework for enhancing food safety against bioterrorism agents and supports the development of resilient agricultural practices.

Consecutive stresses, such as initial submergence during germination followed by water deficit during the seedling stage, pose significant challenges to direct-seeded rice cultivation. By Linkage disequilibrium analysis, Sub1 and Dro1 (Δbp: 10 Mb), as well as Sub1 and TPP7 (Δbp: 6 Mb) were identified to exhibit long-range linkage disequilibrium (LRLD). Meta-QTL analysis further revealed that Sub1 and TPP7 co-segregated for tolerance to submergence at the germination and seedling stages. Based on this, we hypothesized that LRLD might influence plant responses to consecutive stresses. To test this hypothesis, we developed a structured recombinant inbred line population from a cross between Bhalum 2 and Nagina 22, with alleles (Sub1 and TPP7) in linkage equilibrium. Mendelian randomization analysis validated that the parental alleles, rather than the recombinant alleles of Sub1 and TPP7, significantly influenced 13 out of 41 traits under consecutive stress conditions. Additionally, 16 minor additive effect QTLs were detected between the genomic regions, spanning Sub1 and TPP7 for various traits. A single allele difference between these genomic regions enhanced crown root number, root dry weight, and specific root area by 11.45%, 15.69%, and 33.15%, respectively, under flooded germination conditions. Candidate gene analysis identified WAK79 and MRLK59 as regulators of stress responses during flooded germination, recovery, and subsequent water deficit conditions. These findings highlight the critical role of parental allele combinations and genomic regions between Sub1 and TPP7 in regulating the stress responses under consecutive stresses. Favourable haplotypes derived from these alleles can be utilized to improve stress resilience in direct-seeded rice.

Dopamine β-monooxygenase N-terminal (DOMON) domain-containing genes are present across all taxa and are critical in cell signaling and redox transport. Despite their significance, these genes remain understudied in plant species. In this study, we identified 15 DOMON genes in rice and analyzed their phylogenetic relationships, conserved motifs, and cis-regulatory elements. Phylogenetic analysis revealed distinct clustering of OsDOMON genes in rice and other monocots, compared with Arabidopsis thaliana. Promoter analysis showed a higher abundance of stress-related regulatory elements in Tetep, a well-known blast and abiotic stress-tolerant cultivar, compared with Nipponbare and HP2216. OsDOMON genes displayed differential expression under biotic stress (Magnaporthe oryzae infection) and abiotic stresses (drought, heat, and salinity) in contrasting cultivars. Tetep exhibited significantly higher expression levels of specific OsDOMON genes during early blast infection stages, particularly OsDOMON6.1 and OsDOMON9.2, suggesting their roles in cell wall fortification and reactive oxygen species signaling. Under abiotic stress, genes like OsDOMON3.3, OsDOMON8.1, and OsDOMON9.2 showed higher expression in Tetep, indicating their involvement in stress tolerance mechanisms. This study provides a foundation for future functional studies of OsDOMON genes, paving the way for developing rice cultivars resistant to biotic and abiotic stresses.

The wall-associated kinases (WAKs) play a crucial role in rice resistance, but their relationship to yield-related traits remains poorly understood. In this study, we analyzed the rice wall-associated kinase galacturonan-binding (WAKg) gene family and evaluated its association with both disease resistance and grain yield. A total of 108 OsWAKg genes were identified in rice. Promoter cis-element analysis revealed that the promoter regions of OsWAKg genes contain abundant resistance- and hormone-related elements. Induced expression analysis of 18 OsWAKg genes highly expressed in both rice leaves and roots showed that 14 genes were pathogen-induced, 9 were induced by development-related hormones, and 8 were responded to both stimuli. Transgenic validation confirmed that OsWAKg16 and OsWAKg52 positively regulate rice disease resistance and yield. Moreover, OsWAKg52 regulates rice disease resistance through multiple pattern-triggered immunity responses. These findings demonstrate that OsWAKgs significantly contribute to the coordinated regulation of disease resistance and grain yield, providing new insights into rice WAKg gene family and potential genetic resources for synergistic crop improvement.

Rice, a critical global staple crop, relies heavily on heading date, a key agronomic trait marking the transition from vegetative to reproductive growth. Understanding the genetic regulation of heading date is vital for enhancing the adaptability of high-quality rice varieties across diverse geographical regions and for bolstering local food security. In this study, we uncovered a novel role for OsCATA, a catalase gene, in the regulation of photoperiodic flowering in rice. We identified a novel allele of OsELF3.1, whose mutation resulted in delayed heading. Further analyses revealed that OsELF3.1 physically interacted with OsCATA. Notably, OsCATA exhibited rhythmic expression patterns similar to OsELF3.1 and, when mutated, also delayed flowering. Expression analyses showed that the delayed heading phenotype could be attributed to elevated Ghd7 expression under both long-day and short-day conditions, with OsCATA expression positively regulated by OsELF3.1. Double mutants of OsELF3.1 and OsCATA displayed a heading delay similar to that of oself3.1 single mutants. Additionally, OsELF3.1 could interact with Ghd7 in vivo, alleviating its suppression of Ehd1. Luciferase assays confirmed that Ghd7 repressed Ehd1 expression, while OsELF3.1 mitigated this repression. Collectively, our findings reveal that OsCATA is critical in suppressing Ghd7 expression through the OsELF3.1-OsCATA-Ghd7 transcriptional pathway, thereby regulating rice heading.

The ubiquitin-proteasome system involves three types of enzymes (E1, E2, and E3) that promote protein ubiquitination and degradation. Among these, the E3 ubiquitin ligase mediates substrate specificity. In rice, over 1 500 E3 enzymes have been identified, playing diverse roles in growth, developmental processes, and responses to biotic and abiotic stresses. In recent years, significant progress has been made, with some breakthroughs in regulating disease resistance. Here, we summarize the roles of rice E3 ubiquitin ligases in responding to biotic and abiotic stresses, as well as their functions in regulating key agronomic traits such as seed size. Additionally, future research directions are discussed. This review aims to facilitate further studies on E3 ubiquitin ligases in rice.

Straw burning has emerged as a persistent and multifaceted challenge within global agricultural systems, particularly across Asia, Africa, and Latin America. This review reframes straw burning not as an isolated behavioral issue, but as the outcome of interlinked structural, technological, and socio-cultural constraints embedded in modern agricultural transitions. Drawing on a synthesis of recent empirical studies, we identify four conceptual turning points that reshape the understanding of straw burning: the structural consequences of mechanization, the trade-offs between high- and low-tech solutions, the cultural legitimacy of burning practices, and the need for systems-based, climate-aligned management paradigms. The analysis reveals that interventions focusing solely on technical innovation often overlook the deeper institutional and cultural factors that sustain burning as a rational choice under constrained conditions. We advocate for hybrid, place-based strategies that combine accessible agronomic practices with long-term investments in infrastructure, policy alignment, and community engagement. Moving beyond fragmented solutions and adopting an integrated systems lens enables this study to contribute a forward-looking framework for sustainable straw management that is environmentally just, socially legitimate, and economically viable.

Male gametes are produced in the anthers and are essential for fertilization and seed setting. A transverse section of the anther reveals four layers: the epidermis, endothecium, middle layer, and tapetum. The tapetum, being the innermost layer, plays a critical role in supplying nutrients, enzymes, and protection to microspores. Detailed microscopic and ultrastructural analyses have revealed highly active and well-organized structures within the tapetum, referred to as tapetal organelles. Molecular studies have highlighted the significance of tapetal cell death and the nurturing role of the tapetum for microspores. However, the mechanisms by which these processes are mediated by tapetal organelles at the cellular level remain elusive. The discovery of mutants defective in tapetal organelles has enabled further investigations into their structure, morphology, and function. This review discusses the molecular and functional roles of various tapetal organelles, such as plastids (amyloplasts and elaioplasts), mitochondria, tapetosomes, endoplasmic reticulum, and lipid bodies. We provide an overview of their roles, highlight key organelles in the tapetum, and address recent challenges and potential applications of genes regulating tapetal organelles in enhancing crop fertility.

Bacterial blight (BB) is a devastating worldwide rice disease caused by Xanthomonas oryzae pv. oryzae (Xoo), which is difficult to diagnose based on early symptoms. Conventional chemical control yields limited effectiveness once BB has spread. Consequently, it is imperative to develop a rapid, highly sensitive, specific, and easy-to-use detection technique for early on-site diagnosis of BB. We first developed a recombinase-aided amplification-lateral flow dipstick (RAA-LFD) technique for the on-site detection of Xoo. The optimized reaction temperature and time were 37 ºC and 20 min, indicating that the reaction system can be initiated by body temperature independently of any precision instruments. Evaluation of the RAA-LFD technique using the primers (RAAF2/R2) and probe (RAA2-nfo-probe) derived from the XooORF0080 locus exhibited high specificity and eliminated cross-reactivity with other bacterial species. The sensitivity of RAA-LFD is up to 1 pg/μL for Xoo genomic DNA and 100 CFU/mL for Xoo cells. Significantly, this technique accurately detected Xoo from both artificially inoculated and naturally infected rice leaves at the early stage of infection, directly deploying plant tissue fluid as the template without DNA extraction. These attributes make the developed RAA-LFD system a viable technique for the early diagnosis of BB in the field, providing technical support for early-warning systems and disease control.