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

High salt content in soils severely hampers plant growth and crop yields. Many transcription factors in plants play important roles in responding to various stresses, but their molecular mechanisms remain unclear. WRKY transcription factors are one of the largest families of transcription factors in higher plants that are involved in and influence many aspects of plant growth and development. They play important roles in responding to salt stress. The regulation of gene expression by WRKY proteins is mainly achieved by binding to the DNA's specific cis-regulatory elements, the W-box elements (TTGACC). In recent years, there have been many studies revealing the roles and mechanisms of WRKY family members, from model plant Arabidopsis to agricultural crops. This paper reviews the latest research progress on WRKY transcription factors in response to salt stress and discusses the current challenges and future perspectives of WRKY transcription factor research.
Transcription Factors/metabolism* ; Plant Proteins/metabolism* ; Stress, Physiological/genetics* ; Salt Stress/genetics* ; Crops, Agricultural/genetics* ; Gene Expression Regulation, Plant ; Phylogeny ; Plants, Genetically Modified/genetics*

The World Health Organization (WHO) defines health as "a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity" (WHO, 2017), and mental health is defined as not only the absence of mental illness, but also the presence of psychological well-being. An expanding body of evidence highlights the relationship between nature (such as urban greenspace) and health (Li et al., 2019; Flaxman et al., 2020). However, human development and subsequent effects such as climate change and epidemic disease (COVID-19) lead to altered living environments and lifestyles. Expanding cities and urban residents have inequitable access to nature, particularly in areas of greater depriv‑ation, where both public and private greenspaces are less available (Feng et al., 2021). In addition, young people spend more than 80% of their time indoors due to constant use of electronic devices for work, study, and entertainment (Klepeis et al., 2001). Mobile phones, personal computers, and video-game devices have become the main means for them to release stress. Excessive use of these electronic devices may affect normal brain activity, increasing the risk of Internet addiction and producing a range of physical and mental problems (Tran et al., 2017). These signal the pressing need for scientific investigation of efficient and convenient ways to increase contact with nature, or alternatively, to better regulate emotions indoors.
Adolescent ; Humans ; East Asian People ; Plants, Edible ; Food Preferences