Search Results (2,461)

Individual recognition of Hu sheep is a core requirement for precision livestock management, significantly improving breeding efficiency and fine management. However, traditional machine vision methods face challenges such as high annotation time costs, the inability to quickly annotate new sheep, and the need for manual intervention and retraining. To address these issues, this study proposes a solution that integrates automatic annotation and transfer learning, developing a sheep face recognition algorithm that adapts to complex farming environments and can quickly learn the characteristics of new Hu sheep individuals. First, through multi-view video collection and data augmentation, a dataset consisting of 82 Hu sheep and a total of 6055 images was created. Additionally, a sheep face detection and automatic annotation algorithm was designed, reducing the annotation time per image to 0.014 min compared to traditional manual annotation. Next, the YOLOv10n-CF-Lite model is proposed, which improved the recognition precision of Hu sheep faces to 92.3%, and the mAP@0.5 to 96.2%. To enhance the model’s adaptability and generalization ability for new sheep, transfer learning was applied to transfer the YOLOv10n-CF-Lite model trained on the source domain (82 Hu sheep) to the target domain (10 new Hu sheep). The recognition precision in the target domain increased from 91.2% to 94.9%, and the mAP@0.5 improved from 96.3% to 97%. Additionally, the model’s convergence speed was improved, reducing the number of training epochs required for fitting from 43 to 14. In summary, the Hu sheep face recognition algorithm proposed in this study improves annotation efficiency, recognition precision, and convergence speed through automatic annotation and transfer learning. It can quickly adapt to the characteristics of new sheep individuals, providing an efficient and reliable technical solution for the intelligent management of livestock. Full article

The black soldier fly, BSF (Hermetia illucens) has extensive applications in insect protein production and organic waste conversion, serving as a crucial resource insect. However, large-scale breeding faces challenges such as low adult mating rates, unstable oviposition, and inefficient egg collection, which significantly hinder the industrial application of BSF. In this study, we aimed to enhance the oviposition efficiency of BSF by utilizing the microbes within it. We isolated a strain of Serratia marcescens from the ovaries of the BSF, which can attract BSF to lay eggs by producing dimethyl disulfide. Genome analysis of the bacterium revealed a total length of 5,244,812 bp with a GC content of 59.18%. Based on KEGG database annotations, 5486 genes were identified through genome sequencing. The findings of this study provide a theoretical foundation for enhancing BSF production efficiency and offer insights for further microbial regulation development. Full article

Diadema setosum is an economically important species in tropical and subtropical waters. To determine the optimal salinity for D. setosum aquaculture, we examined six salinity levels (20, 24, 28, 32, 36, and 40) during winter and spring, assessing their effects on survival, growth, and eco-physiological parameters of juvenile D. setosum. Results showed that (1) in winter, the survival rate of juvenile D. setosum was highest at salinities of 28–36, with 100% survival at salinities of 32–36. During spring, all salinity groups reached 100% survival. (2) Juveniles exhibited optimal growth performances at salinities of 32–36 across all seasons, and negative growth occurred at lower salinities (20–24) during winter. (3) The juveniles exhibited higher oxygen consumption rate and ammonium excretion rate with an atomic O:N ratio > 25 at salinities of 32–36, indicating that carbon-based substrates were the primary catabolic substrate. Under salinity stress (<24 or >40), the O:N ratio declined significantly, reflecting that proteins were the main metabolic substrate. (4) Fecal excretion was higher in winter than in spring, possibly due to size and digestive efficiency differences. Studies confirmed that salinity and temperature exert significantly combined effects on D. setosum, with an optimal salinity range of 32–36. This work provides valuable insights for the breeding and aquaculture of this species. Full article

The development of a new salt–alkaline-tolerant hybrid japonica rice is crucial for enhancing japonica rice supply and ensuring national food security. Utilizing molecular marker-assisted selection (MAS) technology combining Kompetitive Allele-Specific PCR (KASP) markers and a gene breeding chip, the salt-tolerant gene SKC1 was introgressed into a rice genotype Fan 14. This led to the development of Shenyanhui 1, a new high-quality, strongly heterotic, and salt-tolerant japonica restorer line. Subsequently, the high-quality, salt-tolerant japonica three-line hybrid rice variety Shenyanyou 1 was developed by crossing the BT-type japonica cytoplasmic male sterile (CMS) line Shen 21A with the restorer line Shenyanhui 1. Shenyanyou 1 carries the major salt tolerance gene SKC1, exhibiting excellent salt tolerance with seedling stage salt tolerance reaching level 5. Under precise salt tolerance evaluation throughout its growth cycle, Shenyanyou 1 achieved a yield of 3640.5 kg/hm², representing an extremely significant increase of 20.7% over the control variety Yandao 21. Shenyanyou 1 exhibits superior grain quality, meeting the Grade 3 high-quality rice standards issued by the Ministry of Agriculture. Shenyanyou 1 has good comprehensive resistance, aggregating rice blast resistance genes such as Pi2, Pita, Pizt and LHCB5, bacterial blight resistance genes Xa26/Xa3, stripe blast resistance gene STV11, semi-dwarf gene Sdt97, nitrogen-efficient utilization gene NRT1.1B, the light repair activity enhancement gene qUVR-10, the cold resistance gene qLTG3-1, and the iron tolerance gene OsFRO1. It has good resistance to biotic and abiotic stresses. This paper details the breeding process, key agronomic traits, salt tolerance, yield performance, and grain quality characteristics of Shenyanyou 1. Full article

Sugar beet (Beta vulgaris L.) is a biennial herbaceous plant belonging to the genus Beta within the family Amaranthaceae. Its root tuber can be used as an effective source for sucrose production. In the pursuit of sustainable development and maximizing the economic value of crops, the full utilization of crop germplasm resources and efficient production is necessary. To better facilitate the collection and utilization of sugar beet germplasm resources, this study used 106 accessions of multigerm sugar beet germplasm provided by the Key Laboratory of Molecular Genetic Breeding for sugar beet as materials. We evaluated the core collections constructed under various strategies using relevant genetic parameters and ultimately established two core collection construction strategies based on morphological and molecular markers. The optimal strategy based on morphological data was “Euclidean distance + Multiple clustering deviation sampling + UPGMA + 25% sampling proportion”, while the optimal strategy based on molecular marker data was “Jaccard distance + Multiple clustering random sampling + UPGMA + 20% sampling proportion”. In addition, representativeness evaluation of the core collection was conducted based on parameters related to both morphology and molecular markers. Principal component analysis (PCA) was utilized for the final determination of the core collection. The results showed that for both the morphological parameters and molecular marker-related parameters, there were no significant differences between the constructed core collection and the original germplasm; the phenotypic distribution frequencies were basically similar. Principal component analysis indicated that the core collection possessed a population structure similar to that of the original germplasm. The constructed core collection had good representativeness. This study, for the first time, proposed a core collection construction approach suitable for sugar beet by integrating morphological and molecular marker methodologies. It aimed to provide a scientific basis for the utilization and development of sugar beet germplasm resources, genetic improvement, and the breeding of new cultivars. Full article

Understanding the genetic basis of growth and feed efficiency traits is essential for advancing beef cattle breeding programs. This study analyzed the average daily gain (ADG; from birth [day 0] to 200 days of age) and the Kleiber ratio (KR) in Charolais and Limousine calves raised in Romania. The data collection period was between 2020 and 2022. Genetic parameters were estimated using a maternal animal model based on 936 Charolais and 726 Limousine records sourced from the Romanian Breeding Association. For both traits, Charolais showed lower direct, maternal and total heritability estimates (0.44, 0.17 and 0.44 for ADG; 0.44, 0.17 and 0.44 for KR) compared to Limousine (0.67, 0.26 and 0.67 for ADG; 0.66, 0.26 and 0.67 for KR). The sex of calf and season of birth influenced the average daily gain and Kleiber ratio. Strong correlations were observed between the average daily gain and Kleiber ratio. The Kleiber ratio was confirmed as a reliable genetic indicator of feed efficiency across both breeds. Full article

Rapid and accurate identification of maize architecture characteristics is important for understanding both yield potential and crop breeding experiments. Most canopy architecture indicators cannot fully reflect the vertical leaf distribution in field environments. We conducted field experiments on sixty maize cultivars under four planting densities at three different sites, and herein introduce two novel indicators, “kurtosis and skewness,” based on the manually measured leaf area index (LAI) of maize at five different canopy heights. Then, we constructed the LAI, plant height (PH), kurtosis, and skewness estimation models based on unmanned aerial vehicle multispectral, RGB, and laser detecting and ranging data, and further assessed the canopy architecture and estimated yield. The results showed that the fitting coefficient of determination (R²) of cumulative LAI values reached above 0.97, and the R² of the four indicators’ estimation models based on multi-source data were all above 0.79. A high LAI, along with greater kurtosis and skewness, optimal PH levels, and strong stay-green ability, are essential characteristics of high-yield maize. Moreover, the four indicators demonstrated high accuracy in estimating yield, with the R² values based on measured canopy indicators at the four planting densities being 0.792, 0.779, 0.796, and 0.865, respectively. Similarly, the R² values for estimated yield based on estimated canopy indicators were 0.636, 0.688, 0.716, and 0.775, respectively. These findings provide novel insight into maize architecture characteristics that have potential application prospects for efficient estimation of maize yield and the breeding of ideal canopy architecture. Full article

Genetic improvement, including improving the growth rate, survival rate and feed efficiency ratio (FER), can maximize the production efficiency of turbot. Genetic evaluations of the growth rate, survival rate and FER are required for determining the practicability of including these three traits in a breeding programme. In this study, 20 full-sib families were produced. A method involving a small sample was used to calculate the growth rate, survival rate and FER to represent the corresponding data of an individual. Then, the genetic parameters of the three economic traits were evaluated. The heritabilities for the survival rate, growth rate and FER in turbot were 0.109001, 0.232335 and 0.101866, respectively. The heritabilities of the survival rate and FER were low, while that of the growth rate was moderate. The phenotypic correlations between the survival rate and growth rate, the survival rate and FER, and the survival rate and FER were 0.0919, 0.4609 and 0.2472, respectively. The genetic correlations between the growth rate and survival rate, the survival rate and FER, and the growth rate and FER were 0.0984, 0.3732 and 0.5654, respectively. Apart from a low phenotypic and genetic correlation between the survival rate and growth rate, the correlations among the other traits were moderate. Full article

Soybean (Glycine max L.) is an essential crop for global food, feed, and industrial applications, but its production is increasingly challenged by climate change and environmental stresses. Traditional breeding and transgenic approaches have contributed to improvements in yield and quality; however, limitations in genetic diversity and regulatory hurdles for genetically modified organisms (GMOs) underscore the need for innovative strategies to address these challenges. Genome editing technologies, particularly CRISPR/Cas9, have revolutionized soybean molecular breeding by enabling precise modifications of genes related to key agronomic traits such as yield, seed composition, and stress tolerance. These advances have accelerated the development of soybean varieties with enhanced nutritional value and adaptability. Recent progress includes improvements in editing efficiency, specificity, and the ability to target multiple genes simultaneously. However, the application of genome editing remains concentrated in a few model cultivars, and challenges persist in optimizing transformation protocols, minimizing off-target effects, and validating edited traits under field conditions. Future directions involve expanding the genetic base, integrating genome editing with synthetic biology, and addressing regulatory and public acceptance issues. Overall, genome editing offers significant potential for sustainable soybean improvement, supporting food security and agricultural resilience in the face of global challenges. Full article

The advancement of high-throughput sequencing has revolutionised genomic research by generating large amounts of data. However, Whole-Genome Sequencing is associated with a statistical challenge known as the p >> n problem. We classified 1825 individuals into five breeds based on 11,915,233 SNPs. First, three feature selection algorithms were applied: SNP-tagging and two approaches based on supervised rank aggregation, followed by either one-dimensional (1D-SRA) or multidimensional (MD-SRA) feature clustering. Individuals were then classified into breeds using a deep learning classifier composed of Convolutional Neural Networks. SNPs selected by SNP-tagging yielded the least satisfactory F1-score (86.87%); however, this approach offered rapid computing time. The 1D-SRA was less suitable for ultra-high-dimensional data due to computational, memory, and storage limitations. However, the SNP set selected by this algorithm provided the best classification quality (96.81%). MD-SRA provided a good balance between classification quality (95.12%) and computational efficiency (17x lower analysis time, 14x lower data storage). Unlike SNP-tagging, SRA-based approaches are universal and are not limited to genomic data. This study addressed the demand for efficient computational and statistical tools for feature selection in high-dimensional genomic data. The results demonstrate that the proposed MD-SRA is suitable for the classification of high-dimensional data. Full article

Plant architecture is a crucial agronomic trait significantly impacting soybean (Glycine max) yield. Traditional breeding has made some progress in optimizing soybean architecture, but it is limited in precision and efficiency. The Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein (CRISPR/Cas) system, a revolutionary gene-editing technology, provides unprecedented opportunities for plant genetic improvement. This review outlines CRISPR’s development and applications in crop improvement, focusing specifically on progress regulating soybean architecture traits affecting yield, such as node number, internode length, branching, and leaf morphology. It also discusses the technical challenges for CRISPR technology in enhancing soybean architecture, including that the regulatory network of soybean plant architecture is complex and the development of multi-omics platforms helps gene mining. The application of CRISPR enables precise the regulation of gene expression through promoter editing. Meanwhile, it is also faced with technical challenges such as the editing of homologous genes caused by genome polyploidy, the efficiency of editing tools and off-target effects, and low transformation efficiency. New delivery systems such as virus-induced genome editing bring hope for solving some of these problems. The review emphasizes the great potential of CRISPR technology in breeding next-generation soybean varieties with optimized architecture to boost yield potential. Full article

Prime editing (PE), a novel “search-and-replace” genome editing technology, demonstrates significant potential for crop genetic improvement due to its precision and versatility. However, since its initial application in plants, PE technology has consistently faced challenges of low and variable editing efficiency, representing a major bottleneck hindering its broader application. Therefore, this study conducted a systematic review following the PRISMA 2020 guidelines. We systematically searched databases—Web of Science, PubMed, and Google Scholar—for studies published up to June 2025 focusing on enhancing PE performance in crops. After a rigorous screening process, 38 eligible primary research articles were ultimately included for comprehensive analysis. Our analysis revealed that early PE systems such as PE2 could perform diverse edits, including all 12 base substitutions and small insertions or deletions (indels), but their efficiency was highly variable across species, targets, and edit types. To overcome this bottleneck, researchers developed four major optimization strategies: (1) engineering core components such as Cas9, reverse transcriptase (RT), and editor architecture; (2) enhancing expression and delivery via optimized promoters and vectors; (3) improving reaction processes by modulating DNA repair pathways or external conditions; and (4) enriching edited events through selectable or visual markers. These advancements broadened PE’s targeting scope with novel Cas9 variants and enabled complex, kilobase-scale DNA insertions and rearrangements. The application of PE technology in plants has evolved from basic functional validation, through systematic optimization for enhanced efficiency, to advanced stages of functional expansion. This review charts this trajectory and clarifies the key strategies driving these advancements. We posit that future breakthroughs will increasingly depend on synergistically integrating these strategies to enable the efficient, precise, and predictable application of PE technology across diverse crops and complex breeding objectives. This study provides an important theoretical framework and practical guidance for subsequent research and application in this field. Full article

Tomato is one of the most important vegetables due to its high production and nutritional value. With the development of digital agriculture, the tomato breeding and processing industries have seen a rapid increase in the need for simple, low-labor, and inexpensive methods for analyzing tomato composition. This study proposes a digital method to predict four minerals (calcium, potassium, phosphorus, and magnesium) in beef-type tomato using machine learning models, including k-nearest neighbors (kNN), artificial neural networks (ANNs), and Support Vector Regression (SVR). The models were discriminated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The kNN model showed the best performance for estimation of quantity of calcium, potassium, phosphorus, and magnesium. The results demonstrate that kNN consistently outperforms ANNs and SVR across all target nutrients, achieving the highest R² and the lowest error metrics (RMSE, MAE, and MAPE). Notably, kNN achieved an exceptional R² of 0.8723 and a remarkably low MAPE of 3.95% in predicting phosphorus. This study highlights how machine learning can provide a versatile, accurate, and efficient solution for tomato mineral analysis in digital agriculture. Full article

Spectral reflectance of plants can be readily associated with physiological and biochemical parameters. Thus, relating spectral data to amino acid contents in different genetic materials provides an innovative and efficient approach for understanding and managing genetic diversity. Therefore, this study had two objectives: (I) to differentiate genetic materials according to amino acid contents and spectral reflectance; (II) to establish the relationship between amino acids and spectral bands derived from hyperspectral data. The research was conducted with 32 soybean genetic materials grown in the field during the 2023–2024 crop year. The experimental design involved randomized blocks with four replicates. Leaf spectral data were collected 60 days after plant emergence, when the plants were in full bloom. Three leaf samples were collected from the third fully developed trifoliate leaf, counted from top to bottom, from each plot. The samples were taken to the laboratory, where reflectance readings were obtained using a spectroradiometer, which can measure the 350–2500 nm spectrum. Wavelengths were grouped as means of representative intervals and then organized into 28 bands. Subsequently, the leaf samples from each plot were subjected to quantification analyses for 17 amino acids. Then, the soybean genotypes were subjected to a PCA–K-means analysis to separate the genotypes according to their amino acid content and spectral behavior. A correlation network was constructed to investigate the relationships between the spectral variables and between the amino acids within each group. The groups formed by the different genetic materials exhibited distinct profiles in both amino acid composition and spectral behavior. Leaf reflectance data proved to be efficient in identifying differences between soybean genotypes regarding the amino acid content in the leaves. Leaf reflectance was effective in distinguishing soybean genotypes according to leaf amino acid content. Specific and high-magnitude associations were found between spectral bands and amino acids. Our findings reveal that spectral reflectance can serve as a reliable, non-destructive indicator of amino acid composition in soybean leaves, supporting advanced phenotyping and selection in breeding programs. Full article

The objective of this study was to characterize the sexual behavior and reproductive performance of Colombian Creole bulls from the Romosinuano (ROM) and Costeño con Cuernos (CCC) breeds, to support their strategic use in tropical production systems and sire selection programs. A standardized sexual behavior test, including nine behavioral indicators, was conducted over a 15 min observation period to assess libido and service capacity. Significant differences (p < 0.05) were found between the breeds in terms of the frequency of urination and mounting behaviors. ROM bulls exhibited a more uniform and rapid behavioral response, while CCC bulls showed greater individual variability and a broader behavioral repertoire, with courtship behaviors—such as smelling, the Flehmen reflex, and butting—strongly associated with ejaculation events. Libido scores were high in both breeds, with 80.35% of bulls rated as very good to excellent. CCC bulls also achieved mounts more frequently within the first five minutes of exposure. Additionally, bull age was inversely associated with mounting time (p < 0.05), suggesting that maturity and sexual experience influence behavioral efficiency. These findings represent the first quantitative assessment of sexual behavior in CCC bulls and provide comparative insights with ROM bulls, highlighting the functional reproductive potential of Colombian Creole bulls under low-input tropical conditions. Full article