In recent years, UAV remote sensing technology (UAV-RS) has been widely used in agricultural monitoring, especially in managing gramineous crops. Gramineous crops like wheat, rice, maize, sorghum, and sugarcane are vital global food and energy sources. They are crucial for food security and agricultural economies. The integration of UAV-RS with computer vision (CV), machine learning (ML), and deep learning (DL) has enabled the precise monitoring of these crops across various growth stages. Notably, researchers have made significant progress in monitoring key agronomic parameters such as plant height, leaf area index, nitrogen nutritional status, aboveground biomass, and crop growth status indicators such as lodging, plant detection, yield prediction, and pest and disease monitoring. This study offers a systematic review of the recent advancements in the application of UAV-RS for agricultural information monitoring in growing wheat, rice, maize, sorghum, and sugarcane since 2020. The focus is on summarizing remote sensing methods for monitoring various agronomic parameters and crop growth status. Furthermore, it discusses the interrelationships among these monitoring parameters. This provides a theoretical foundation for developing comprehensive monitoring models. By considering the phenological characteristics of different crops, this study also identifies the optimal timing for monitoring key parameters. This offers scientific guidance for optimizing monitoring strategies. Finally, we address current research limitations and propose future research directions.

Objective This study aimed to explore the anti-aging effects and mechanisms of Dendrobium nobile Lindl. Alkaloids (DNLA) on Caenorhabditis elegans (C. elegans). Methods The effects of DNLA on the lifespan of C. elegans under normal, oxidative stress and heat stress conditions were determined. Moreover, the deposition of lipofuscin in the intestinal tract, the mobility, pharyngeal pumping rate and SOD activity of C. elegans were assayed. The daf-16 mRNA expression levels of aging-related genes and nuclear localization of DAF-16 were tested. Furthermore, lifespan tests were conducted in CF1038[daf-16(mu86)] and EU1[skn-1(zu67)] mutant nematodes. Results The lifespan of C. elegans were prolonged after DNLA administration under normal conditions, oxidative stress and thermal stress, and the optimal concentration of DNLA was 0.35 μg/mL. In addition, DNLA significantly reduced lipofuscin deposition, enhanced mobility and increased SOD activity of C. elegans. The mRNA expression of daf-16, skn-1, sod-3, hsp-16.2 and ctl-1 were increased after 0.35 μg/mL DNLA treatment. DNLA also reduced cytoplasmic DAF-16 and increased its intermediate and nuclear distributions. The longevity promotion effect of DNLA on C. elegans disappeared after daf-16 or skn-1 mutation. After deletion of daf-16 or skn-1, the effects of DNLA on C. elegans were lost. Conclusions DNLA can prolong the lifespan and stress resistance of C. elegans. The mechanism may be related to the up-regulation of daf-16 and skn-1, which further increases the activity of SOD.

Pathogenic bacteria utilize a type III secretion system (T3SS) to inject type III effectors (T3Es) into plant cells, suppressing plant immunity and facilitating colonization. Paracidovorax citrulli, the causal agent of bacterial fruit blotch (BFB) of Cucurbitaceae crops, harbors a functional T3SS like many other plant pathogens. The expression of its T3SS and T3Es is regulated by the two-component system response regulators HrpG and HrpX. Here, we demonstrate that the aspartic acid (Asp) residues at positions 52 and 60 in P. citrulli HrpG are essential for its complete function. Plasmid-mediated complementation of the ΔhrpG mutant with hrpG carrying Asp52→alanine (Ala) or Asp60→Ala mutations failed to restore the ability of P. citrulli to induce a hypersensitive response (HR) in tobacco, whereas the Asp46→Ala mutation fully rescued this phenotype. Furthermore, genomic hrpG point mutations generating strains Aac5 (D52A) and Aac5 (D60A) abolish the activation of hrpX transcription, resulting in decreased HrpX accumulation. Collectively, Asp 52 and Asp 60 in P. citrulli HrpG are essential for transcriptional activation activity of hrpX and HR induction, serving as a potential phosphorylation site (Asp 52) for upstream histidine kinases and a Mg2+ coordination site (Asp 60). Given that conserved Asp residues often function as phosphorylation sites in two-component system response regulators, this study provides a foundation for identifying upstream histidine kinases that modulate HrpG activity in P. citrulli.

The codling moth, Cydia pomonella (Lepidoptera: Tortricidae), is a significant global pest affecting pome fruit production, thereby necessitating the development of innovative and effective pest management control strategies. In this study, we targeted the white gene (Cpwhite) which is well-characterized in Drosophila and frequently used in non-model insects for functional genetics, due to its critical role in eye pigmentation. Using the CRISPR/Cas9 system, 100% somatic mutagenesis efficiency and a 42.86% germline transmission rate were achieved at the targeted loci, setting up an excellent gene editing platform for future studies. Importantly, a homozygous Cpwhite mutant strain was successfully generated, allowing investigation into the gene's function in pigmentation across developmental stages. Homozygous individuals exhibited obvious phenotypic markers including translucent larval cuticles and complete absence of eye pigmentation in both pupal and adult stages. Therefore, this strain can potentially serve as a transformation marker for genetic studies. Additionally, we assessed the impact of Cpwhite disruption on reproductive parameters, including the fecundity and effective population survival rate. The results suggested that Cpwhite has recessive and crucial effects on eye pigmentation as well as reproduction and fitness. Collectively, this work provides insights into the genetic regulation of key physiological traits of Cpwhite and reveals its potential application as a phenotypic marker in developing genetic strains for pest control strategies.

Tomato is one of the most widely cultivated and consumed vegetables in the world, and its production is severely threatened by bacterial wilt. Bacterial wilt caused by Ralstonia solanacearum, is one of the most devastating plant diseases. R. solanacearum is a complex species with both virulent and avirulent strains. The avirulent strains show high biocontrol activity against bacterial wilt. A metabolomics study was conducted on tomato root exudates induced by R. solanacearum of different pathogenicity, and the potential bacterial wilt resistance metabolites were screened. Principal component analysis revealed a clear separation of the metabolic profile between the virulent strain-induced group, the avirulent strain-induced group, and the CK group. Based on the altered abundance in root exudates after R. solanacearum induction, the most differential metabolites were selected for further investigation, including citramalic acid, glucuronic acid, alpha-ketoglutaric acid, and malic acid, etc. The plate inhibition assay showed that alpha-ketoglutaric acid (AKG) and malic acid (MA) had a dose-dependent inhibitory effect on R. solanacearum (5-20 mg/ml, inhibition circle diameter 14.69-25.08 mm). Crystal violet staining showed that 1-2.5 mg/ml of MA and AKG could significantly inhibit R. solanacearum biofilm formation at 24 h (P < 0.0001, inhibition rate 66.93-70.43%). In tomato pot experiments against bacterial wilt, the AKG group had 75.36% biocontrol efficacy, and the MA group had 57.97% efficacy 25 days after inoculation. We conclude that AKG and MA play an important role in resistance to bacterial wilt in tomato.

We sequenced and assembled the whole genome of Hippotion velox using the Cyclone platform. The initial assembly generated by Canu was subsequently processed with Purge_haplogs to eliminated redundancies, producing a final genome size of 816.16 Mb and a contig N50 of 46.,41 kb. For genome annotation, we integrated evidence from transcriptome data, homology-based comparisons, and de novo gene prediction methods, resulting in a comprehensive set of protein-coding gene models. This complete genomic dataset, including both the assembly and annotation, has been deposited in the China National GeneBank DataBase (CNGBdb) under accession number CNA0505689. The corresponding raw sequencing data are accessible under accession CNP0007417. The availability of the Hippotion velox genome offers a crucial resource for evolutionary comparisons within the Sphingidae family and broader studies on Lepidoptera genome biology.

Metagenomic studies have primarily relied on de novo assembly for reconstructing genes and genomes from microbial mixtures. While reference-guided approaches have been employed in the assembly of single organisms, they have not been used in a metagenomic context. Here, we develop an effective approach for reference-guided metagenomic assembly that can complement and improve upon de novo metagenomic assembly methods for certain organisms. Such approaches will be increasingly useful as more genomes are sequenced and made publicly available.

Insects require different nutrients at different stages of development, understanding these requirements is essential for the effective management of agricultural pests. Hence, the goal of this study is to investigate the molecular mechanisms underlying the diverse nutritional requirements in Spodoptera exigua, an economically important pest of numerous crops and vegetables worldwide. In this study, we found that the preference of early-instar larvae for low-carbohydrate diets is primarily regulated by the CNMamide neuropeptide, whereas late-instar larvae prefer high-carbohydrate diets. Moreover, under high-carbohydrate conditions, both first-and third-instar larvae excreted excess glucose, whereas fifthinstar larvae likely converted glucose to fatty acid and energy. The transcriptome analysis revealed differentially expressed genes (DEGs) associated with the carbohydrate, lipid and energy synthesis and metabolism pathways. Furthermore, all these DEGs were down-regulated in first-and third-instar larvae, whereas over 91% of DEGs were up-regulated in fifthinstar larvae under high-carbohydrate diet. We also found that many genes related to immunity in S. exigua were properly up-regulated via lipid metabolism in early-instar larvae under a high-carbohydrate condition. Interestingly, genes involved in metabolizing exogenous substances were up-regulated as the larvae increased food intake, possibly resulting in enhanced detoxification metabolism in late-instar larvae fed a high-carbohydrate diet. These findings greatly enhance our understanding of the mechanisms underlying carbohydrate preference across larval stages of S. exigua and identify target genes for potential pest control strategies.

The gut microbiota of insects plays a fundamental role in modulating host physiology, including nutrition, development, and adaptability to environmental challenges. The rice water weevil, Lissorhoptrus oryzophilus Kuschel (Coleoptera: Curculionidae), is a major invasive pest of rice worldwide, yet the composition and functional profile of its gut microbial community remain poorly characterized. Here, we employed metagenome sequencing on the Illumina NovaSeq X Plus platform to explore the gut microbial diversity and predicted functions in adults of L. oryzophilus. Our results revealed a rich microbial community, comprising 26 phyla, 42 classes, 72 orders, 111 families, and 191 genera. The bacterial microbiota was overwhelmingly dominated by the phylum Proteobacteria (85.13% of total abundance). At the genus level, Pantoea (48.86%) was the most predominant taxon, followed by Wolbachia (14.57%) and Rickettsia (11.81%). KEGG analysis suggested that the gut microbiota is primarily associated with metabolic pathways such as membrane transport, carbohydrate and amino acid metabolism, cofactor and vitamin metabolism, energy metabolism, and signal transduction. eggNOG annotation further highlighted significant gene representation in amino acid and carbohydrate transport and metabolism, while CAZy annotation revealed glycosyl transferases (GTs) and glycoside hydrolases (GHs) as the dominant carbohydrate-active enzymes. This study provides the first comprehensive insight into the gut microbiome of L. oryzophilus adults, highlighting its potential role in the ecological success of this invasive pest. Our findings lay groundwork for future research aimed at developing novel microbial-based strategies for the sustainable management of L. oryzophilus.

The use of yellow sticky traps is a green pest control method that utilizes the pests’ attraction to the color yellow. The use of yellow sticky traps not only controls pest populations but also enables monitoring, offering a more economical and environmentally friendly alternative to pesticides. However, the small size and dense distribution of pests on yellow sticky traps lead to lower detection accuracy when using lightweight models. On the other hand, large models suffer from longer training times and deployment difficulties, posing challenges for pest detection in the field using edge computing platforms. To address these issues, this paper proposes a lightweight detection method, YOLO-YSTs, based on an improved YOLOv10n model. The method aims to balance pest detection accuracy and model size and has been validated on edge computing platforms. This model incorporates SPD-Conv convolutional modules, the iRMB inverted residual block attention mechanism, and the Inner-SIoU loss function to improve the YOLOv10n network architecture, ultimately addressing the issues of missed and false detections for small and overlapping targets while balancing model speed and accuracy. Experimental results show that the YOLO-YSTs model achieved precision, recall, mAP50, and mAP50–95 values of 83.2%, 83.2%, 86.8%, and 41.3%, respectively, on the yellow sticky trap dataset. The detection speed reached 139 FPS, with GFLOPs at only 8.8. Compared with the YOLOv10n model, the mAP50 improved by 1.7%. Compared with other mainstream object detection models, YOLO-YSTs also achieved the best overall performance. Through improvements to the YOLOv10n model, the accuracy of pest detection on yellow sticky traps was effectively enhanced, and the model demonstrated good detection performance when deployed on edge mobile platforms. In conclusion, the proposed YOLO-YSTs model offers more balanced performance in the detection of pest images on yellow sticky traps. It performs well when deployed on edge mobile platforms, making it of significant importance for field pest monitoring and integrated pest management.

Crofton weed (Ageratina adenophora), a significant invasive species, extensively disrupts ecosystem stability, leading to considerable economic losses. However, genetic insights into its invasive mechanisms have been limited by a lack of genomic data. In this study, we present the successful de novo assembly of the triploid genome of A. adenophora, leveraging long-read PacBio Sequel, optical mapping, and Hi-C sequencing. Our assembly resolved into a haplotype-resolved genome comprising 51 chromosomes, with a total size of ~3.82 Gb and a scaffold N50 of 70.8 Mb. BUSCO analysis confirmed the completeness of 97.71% of genes. Genome annotation revealed 3.16 Gb (76.44%) of repetitive sequences and predicted 123,134 protein-coding genes, with 99.03% functionally annotated. The high-quality reference genome will provide valuable genomic resources for future studies on the evolutionary dynamics and invasive adaptations of A. adenophora.

Elymus sibiricus, a perennial grass of the Poaceae family, plays a vital role in ecological protection and animal husbandry in temperate regions. In this study, an efficient regeneration system was developed using young spikes and mature embryos as explants. Young spikes (0.7–2.6 cm) were cultured on Murashige and Skoog (MS) medium supplemented with 5 mg/L 2,4-Dichlorophenoxyacetic acid (2,4-D) and 0.15 mg/L 6-Benzylaminopurine (6-BA) for callus induction. Callus was then transferred to MS medium containing 1.5 mg/L 2,4-D and 0.15 mg/L 6-BA to promote shoot differentiation. A regeneration system based on young spikes was successfully developed for the variety Gaomu No.1, achieving a regeneration efficiency of 27.55

Amid growing challenges to global food security, high-throughput crop phenotyping has become an essential tool, playing a critical role in genetic improvement, biomass estimation, and disease prevention. Unlike controlled laboratory environments, field-based phenotypic data collection is highly vulnerable to unpredictable factors, significantly complicating the data acquisition process. As a result, the choice of appropriate data collection equipment and processing methods has become a central focus of research. Currently, three key technologies for extracting crop phenotypic parameters are Light Detection and Ranging (LiDAR), Multi-View Stereo (MVS), and depth camera systems. LiDAR is valued for its rapid data acquisition and high-quality point cloud output, despite its substantial cost. MVS offers the potential to combine low-cost deployment with high-resolution point cloud generation, though challenges remain in the complexity and efficiency of point cloud processing. Depth cameras strike a favorable balance between processing speed, accuracy, and cost-effectiveness, yet their performance can be influenced by ambient conditions such as lighting. Data processing techniques primarily involve point cloud denoising, registration, segmentation, and reconstruction. This review summarizes advances over the past five years in 3D reconstruction technologies—focusing on both hardware and point cloud processing methods—with the aim of supporting efficient and accurate 3D phenotype acquisition in high-throughput crop research.

Citrus fruit fungal disease is a major reason for the serious decline in citrus production and quality. Due to its highly contagious nature, timely and effective detection is an important means of prevention and control. Given the high similarity between citrus quarantine diseases and local similar diseases after invading citrus fruits, this study utilizes hyperspectral imaging technology to acquire hyperspectral images of citrus diseases caused by three types of fungi (Phytophthora citrophthora, Phytophthora citricola, Phytophthora syringae). By studying the spectral features of different regions affected by citrus diseases, the competitive adaptive resampling algorithm (CARS) was used to extract 44 feature bands for reconstructing the spectral image, aiming to reduce information redundancy without losing critical information. A simple deep learning model architecture was proposed, which achieved an accuracy of 92.50% in the test dataset. This study provides a new perspective and method for citrus disease detection, offering theoretical and scientific support for the detection of citrus diseases using deep learning and hyperspectral imaging technology.

The genetic network of sex determination in the model organism Drosophila melanogaster was investigated in great detail. Such knowledge not only advances our understanding of the evolution and regulation of sexual dimorphism in insects, but also serves as a basis for developing genetic control strategies for pest species. In this study, we isolated the sex determination gene transformer (Dstra) from a global fruit pest, the spotted-wing Drosophila (Drosophila suzukii), and characterized its gene organization. By comparing the deduced protein sequence of Dstra with its orthologs from 22 species, we found that tra genes from Dipteran species are divergent. Our research demonstrated that Dstra undergoes sex-specific splicing, and we validated its developmental expression profile. We engineered a piggyBac-based transformation vector expressing the complete Dstra coding sequence under the control of the Tetracycline-Off system. Through germ-line transformation, we generated 4 independent transgenic lines, producing pseudo-females from chromosomal males in the absence of tetracycline. This observation indicated ectopic expression of Dstra, confirmed by the detection of female Dstra transcripts in transgenic males. The pseudo-females exhibited altered wing patterns, feminized abdomen, abnormal reproductive organs, and disrupted sexual behavior. Ectopic expression of Dstra affected the sex-specific splicing pattern of the downstream gene fruitless, but not doublesex. In conclusion, our study provides comprehensive genetic, morphological, and behavioral evidence that Dstra controls sexual development in D. suzukii. We discuss the potential applications of this research for genetic control strategies targeting Dstra or using its gene elements.

Meiotic chiasmata are critical for genetic diversity and chromosome segregation. This study aimed to cytologically analyze the variations in chiasmata within the H genome across different ploidy levels, specifically in diploid (Hordeum bogdanii), autotetraploid (Hordeum brevisubulatum), and allotetraploid (Elymus sibiricus) species, to understand the impact of polyploidization. We conducted a comparative cytological analysis of meiotic chiasmata in the H genome of the three species during diakinesis and metaphase I. This study revealed significant variations in the types and frequencies of chromosomal pairing configurations both across different species and among chromosomes within the same species. H. brevisubulatum exhibited a high frequency of quadrivalents. The number of chiasmata in the H genome decreased from 21.32 in H. bogdanii to 19.00 in E. sibiricus and 14.67 in H. brevisubulatum per genome during diakinesis, with a further significant reduction observed at metaphase I. All chromosomes exhibited a similar reduction in chiasmata number from diploid to tetraploid, with the exception of chromosome 1H, which showed a significant increase in E. sibiricus during diakinesis. The frequency of chiasmata significantly decreased from H. bogdanii to E. sibiricus and H. brevisubulatum in both the terminal and interstitial regions. Chiasmata in E. sibiricus and H. brevisubulatum were more terminally localized compared to those in H. bogdanii. However, a significant increase in chiasmata frequency was observed on the short arms of chromosomes 1H and 4H in E. sibiricus during diakinesis. Various patterns of chiasmata localization were observed across the three species during diakinesis. In E. sibiricus, interstitial chiasmata were distributed more distally along both chromosomal arms compared to those in H. bogdanii. In contrast, interstitial chiasmata were absent on the short arms of most chromosomes in H. brevisubulatum and exhibited a more proximal distribution on the long arms. The evolutionary and adaptive significance of these chiasmata variations during polyploidization was further discussed.

Citrus Huanglongbing (HLB), also known as citrus greening, is a severe disease that has caused substantial economic damage to the global citrus industry. Early detection is challenging due to the lack of distinctive early symptoms, making current diagnostic methods often ineffective. Therefore, there is an urgent need for an intelligent and timely detection system for HLB. This study leverages multispectral imagery acquired via unmanned aerial vehicles (UAVs) and deep convolutional neural networks. This study introduce a novel model, MGA-UNet, specifically designed for HLB recognition. This image segmentation model enhances feature transmission by integrating channel attention and spatial attention within the skip connections. Furthermore, this study evaluate the comparative effectiveness of high-resolution and multispectral images in HLB detection, finding that multispectral imagery offers superior performance. To address data imbalance and augment the dataset, this study employ a generative model, DCGAN, for data augmentation, significantly boosting the model’s recognition accuracy. Our proposed model achieved a mIoU of 0.89, a mPA of 0.94, a precision of 0.95, and a recall of 0.94 in identifying diseased trees. The intelligent monitoring method for HLB presented in this study offers a cost-effective and highly accurate solution, holding considerable promise for the early warning of this disease.