Rice is the world's largest food crop, and its yield and quality are directly related to food security and human health. Grain size, as one of the important factors determining the rice yield, has been widely concerned by breeders and researchers for a long time. To decipher the regulatory mechanism of rice grain size, we obtained a multi-tiller, dwarf, and small-grain mutant htd1 by ethyl methanesulfonate (EMS) mutation from the Japonica rice cultivar 'Zhonghua 11' ('ZH11'). Genetic analysis indicated that the phenotype of htd1 was controlled by a single recessive gene. Using the mutation site map (Mutmap) method, we identified the candidate gene OsHTD1, which encoded a carotenoid cleavage dioxygenase involved in the biosynthesis of strigolactone (SL). The SL content in htd1 was significantly lower than that in 'ZH11'. Cytological analysis showed that the grain size of the mutant decreased due to the reductions in the length and width of glume cells. The function of htd1 was further verified by the CRISPR/cas9 gene editing technology. The plants with the gene knockout exhibited similar grain size to the mutant. In addition, gene expression analysis showed that the expression levels of multiple grain size-related genes in the mutant changed significantly, suggesting that HTD1 may interact with other genes regulating grain size. This study provides a new theoretical basis for research on the regulatory mechanism of rice grain size and potential genetic resources for breeding the rice cultivars with high yields.
Oryza/growth & development* ; Mutation ; Edible Grain/growth & development* ; Alleles ; Plant Proteins/genetics* ; Dioxygenases/genetics* ; Lactones/metabolism* ; Gene Expression Regulation, Plant ; Genes, Plant ; Gene Editing ; CRISPR-Cas Systems ; Phenotype

Agriculture, the strategic cornerstone of national long-term stability, is undergoing a fundamental shift from resource-dependent to technology-driven, driven by global food security and ecological conservation needs. Traditional agriculture can no longer sustain the growing food demand. Scientific and technological advancements are fundamental guarantees for ensuring food supply security and are the primary driver for future agricultural development. This special issue compiles the latest research advancements from diverse experts, covering fields such as microbe-driven green agriculture, pesticide technology innovation, intelligent agricultural machinery, smart manufacturing, and molecular design breeding fundamentals. It aims to inspire researchers to explore cutting-edge directions in future agriculture, promote interdisciplinary collaboration and technological integration, and thereby drive innovative breakthroughs and industrial transformation in agricultural modernization.
Agriculture/methods* ; Crops, Agricultural/genetics* ; Food Supply ; Biotechnology ; Pesticides

To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S_p and the total row point count C_total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S_p, average height H_avg, mid-layer point cloud density C_m, and total row point count C_total as input variables, achieved the best performance, with a coefficient of determination (R_v²) of 0.713 and a root mean square error (RMSE_v) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.
Plant Leaves/growth & development* ; Saccharum/growth & development* ; Algorithms ; Unmanned Aerial Devices ; Remote Sensing Technology/methods* ; Crops, Agricultural/growth & development*

Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.
Musa/growth & development* ; Crops, Agricultural/growth & development* ; Algorithms ; Neural Networks, Computer ; Unmanned Aerial Devices ; Seedlings/growth & development* ; Image Processing, Computer-Assisted/methods* ; Photography ; Agriculture/methods*

Genetically modified (GM) crops, as a pivotal innovation in modern agriculture, exhibit significant advantages such as pest and disease resistance, herbicide tolerance, stress tolerance, and yield enhancement. However, their widespread adoption has been associated with potential ecological risks, including weediness of transgenic plants, gene flow, emergence of novel viral strains in virus-resistant crops, impacts on non-target organisms and soil ecosystems, and evolution of target pest resistance. This review focuses on the dual characteristics of GM crops, systematically examining their agronomic benefits and the underlying mechanisms of ecological risks. This review provides a theoretical foundation for optimizing the development of GM crops and ecological risk management, facilitating sustainable agricultural practices.
Plants, Genetically Modified/growth & development* ; Crops, Agricultural/growth & development* ; Ecosystem ; Ecology

Epigenetics refers to a heritable phenomenon that dynamically modulates gene expression without altering the DNA sequence, through molecular mechanisms such as DNA methylation, histone modification, non-coding RNA, chromatin remodeling, and RNA modifications. In plants, these modifications are extensively involved in key biological processes, including flowering time, gametogenesis, stress responses, and immune defenses. Over the past few decades, the research on epigenetics has gradually shifted from fundamental studies primarily conducted in Arabidopsis thaliana to investigations in various crop species such as rice and tomato. This transition has revealed the multifaceted roles of epigenetic regulation in shaping agronomic traits. This review integrates current knowledge of epigenetic regulatory mechanisms and their functions in plant responses to both biotic and abiotic stresses. Epigenetic editing tools such as CRISPR-dCas9 enable targeted DNA methylation or histone acetylation. Emerging transformation technologies, including magnetic nanoparticles and virus-based delivery systems, have the potential to overcome the bottlenecks of plant regeneration, offering new possibilities for precise epigenetic editing. In future agriculture, it is essential to further elucidate multi-layered epigenetic regulatory mechanisms at the single-cell level, develop efficient delivery systems, and leverage artificial intelligence to advance the application of epigenetic breeding for sustainable agricultural development.
Epigenesis, Genetic/genetics* ; Crops, Agricultural/genetics* ; Plant Breeding/methods* ; DNA Methylation/genetics* ; Gene Editing ; Disease Resistance/genetics* ; CRISPR-Cas Systems

Background: The 2019 Expanded National Nutrition Survey results showed that the contribution of fruits and vegetables (FV) in the one-day food intake of adolescents only accounted for 2.6% and 5.9%, respectively. Numerous literatures also stated that insufficient intake of FV is associated with the development of most non-communicable diseases. Objectives: This study was undertaken to compare the knowledge and consumption of FV between selected respondents from public and private SHS in Imus City. Methods: The study used a descriptive research design, and convenience sampling was done to gather respondents. A self-administered questionnaire was developed and the level of knowledge on FV was classified into low, fair, and good. In the evaluation of FV amount consumption, the Daily Nutrition Guide Pyramid for Teens was used. The data were reported in mean, mode, and percentages. Results: More public SHS (49%) obtained a good level of knowledge on nutrient contents of FV, and consumed fruits (61%) more than the recommended daily serving. While more private SHS (96%) had a good level of knowledge of its health benefits but consumed vegetables (67%) less than the recommended daily serving. Their most consumed vegetables were dahon ng sili and garlic. The most consumed fruits were Indian mango and pear. Squash (47%) was the most liked vegetable by both groups. The majority of public SHS identified banana (37%) as their most liked fruit while it was mango (22%) for the majority of private SHS. Public SHS respondents were also recorded to have a lower average estimated family expenditure on FV. Conclusion The study concluded that both public and private SHS do not consume adequate amounts of FV despite their good level of knowledge of its nutrients and health benefits.
Knowledge ; Economics ; Fruit ; Vegetables

Eggplant is an important horticultural crop and one of the most widely grown vegetables in the Solanaceae family. Eggplant fruit-related agronomic traits are complex quantitative traits with low efficiency and long cycle time for traditional breeding selection. With the rapid development of high-throughput sequencing technology and bioinformatics tools, genome-wide association study (GWAS) has shown great application potential in analyzing the genetic rules of complex agronomic traits related to eggplant fruits. This paper first reviews the progress of genome-wide association analysis in eggplant fruit shape, fruit color and other fruit-related agronomic traits. Subsequently, aiming at the problem of missing heritability, which is common in the genetic studies of eggplant quantitative traits, this paper puts forward the development strategies of eggplant GWAS in the future based on the hot spots of application of four GWAS strategies in the research of agronomics traits related to eggplant fruits. Lastly, the application of GWAS strategy in the field of eggplant molecular breeding is expected to provide a theoretical basis and reference for the future use of GWAS to analyze the genetic basis of various eggplant fruit-related traits and to select fruit materials that meet consumer needs.
Solanum melongena/genetics* ; Fruit/genetics* ; Genome-Wide Association Study ; Plant Breeding ; Agriculture ; Vegetables

OBJECTIVE: This study aimed to investigate the association between fruit and vegetable intake and arterial stiffness. METHODS: We conducted a cohort-based study comprising 6,628 participants with arterial stiffness information in the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project. A semi-quantitative food-frequency questionnaire was used to assess baseline (2007-2008) and recent (2018-2021) fruit and vegetable intake. We assessed changes in fruit and vegetable intake from 2007-2008 to 2018-2021 in 6,481 participants. Arterial stiffness was measured using the arterial velocity-pulse index (AVI) and arterial pressure-volume index (API). Elevated AVI and API values were defined according to diverse age reference ranges. RESULTS: Multivariable-adjusted linear regression models revealed that every 100 g/d increment in fruit and vegetable intake was associated with a 0.11 decrease in AVI ( B= -0.11; 95% confidence interval [ CI]: -0.20, -0.02) on average, rather than API ( B = 0.02; 95% CI: -0.09, 0.13). The risk of elevated AVI (odds ratio [ OR] = 0.82; 95% CI: 0.70, 0.97) is 18% lower in individuals with high intake (≥ 500 g/d) than in those with low intake (< 500 g/d). Furthermore, maintaining a high intake in the past median of 11.5 years of follow-up was associated with an even lower risk of elevated AVI compared with a low intake at both baseline and follow-up ( OR = 0.64; 95% CI: 0.49, 0.83). CONCLUSION Fruit and vegetable intake was negatively associated with arterial stiffness, emphasizing recommendations for adherence to fruit and vegetable intake for the prevention of arterial stiffness.
Humans ; Vascular Stiffness ; Fruit ; Vegetables ; Atherosclerosis ; China

Objective: To explore the relationship between nutrient-related dietary pattern and mild cognitive impairment (MCI) in middle-aged and elderly people. Methods: A total of 6 444 middle-aged and elderly people aged ≥55 years were selected in 2018 China Health and Nutrition Survey. MCI was evaluated by Mini-Mental State Examination, and the intakes of various foods were obtained by consecutive 3-day 24-hour dietary survey and weighing method. The intakes of various nutrients and total dietary energy were calculated based on the food composition table. Demographic and social information, lifestyle and health status of the respondents were obtained through questionnaire survey and physical measurements. In this study, vitamin C, vitamin E, zinc, iron, copper and selenium were selected as dependent variables. Nutrient-related dietary patterns were extracted by reduced rank regression method, and the relationship between dietary patterns and MCI was analyzed by multivariate logistic regression model. Results: Six dietary patterns were extracted in this study, and dietary pattern 1 with the highest explanatory degree was selected for subsequent analysis. Dietary pattern 1 was characterized by higher intakes of legume products, vegetables, fruits, nuts, pork, aquatic products and plant oil. Multivariate logistic regression analysis showed that the risk of MCI was lower in Q₄ dietary score group than in Q₁ dietary score group (OR=0.69, 95%CI: 0.49-0.98) in the 55-64 age group. In people with sleep duration of 8 hours per day, the risk of MCI was reduced in Q₂, Q₃ and Q₄ dietary score groups compared with the Q₁ dietary score group, with OR values of 0.68 (95%CI: 0.51-0.92), 0.67 (95%CI: 0.49-0.92) and 0.65 (95%CI: 0.45-0.92), respectively. Interaction analysis showed that the risk for MCI increased in those aged 65-74 years and ≥75 years compared with those aged 55-64 years in Q₁ dietary score group. However, the risk for MCI decreased in both age groups as dietary pattern scores increased. Compared with those with sleep duration less or more than 8 hours per day in Q₁ dietary score group, those with sleep duration of 8 hours per day in Q₂ and Q₃ dietary score groups had a reduced risk for MCI. Conclusion: Dietary patterns with higher intakes of legume products, vegetables, fruits, nuts, pork, aquatic products, and plant oil are negatively associated with MCI in people aged 55-64 years and those who slept 8 hours per day, and may reduce the risk of MCI with aging.
Aged ; Middle Aged ; Humans ; Feeding Behavior/psychology* ; Diet ; Cognitive Dysfunction/epidemiology* ; Nutrients ; Vegetables ; China/epidemiology*