Search Results (94)

The diameter of the sunflower flower head and the thickness of its margins are important crop phenotypic parameters. Traditional, single-dimensional two-dimensional imaging methods often struggle to balance precision with computational efficiency. This paper addresses the limitations of the YOLOv11n-seg model in the instance segmentation of floral disk fine structures by proposing the MBLA-YOLO instance segmentation model, achieving both lightweight efficiency and high accuracy. Building upon this foundation, a non-contact measurement method is proposed that combines an improved model with three-dimensional point cloud analysis to precisely extract key structural parameters of the flower head. First, image annotation is employed to eliminate interference from petals and sepals, whilst instance segmentation models are used to delineate the target region; The segmentation results for the disc surface (front) and edges (sides) are then mapped onto the three-dimensional point cloud space. Target regions are extracted, and following processing, separate models are constructed for the disc surface and edges. Finally, with regard to the differences between the surface and edge structures, targeted methods are employed for their respective calculations. Whilst maintaining lightweight characteristics, the proposed MBLA-YOLO model achieves simultaneous improvements in accuracy and efficiency compared to the baseline YOLOv11n-seg. The introduced CKMB backbone module enhances feature modelling capabilities for complex structural details, whilst the LADH detection head improves small object recognition and boundary segmentation accuracy. Specifically, the CKMB module integrates MBConv and channel attention to strengthen multi-scale feature extraction and representation, while the LADH module adopts a tri-branch design for classification, regression, and IoU prediction, structurally improving detection precision and boundary recognition. This research not only demonstrates superior accuracy and robustness but also significantly reduces computational overhead, thereby achieving an excellent balance between model efficiency and measurement precision. This method avoids the need for three-dimensional reconstruction of the entire plant and multi-view point cloud registration, thereby reducing data redundancy and computational resource expenditure. Full article

Background/Objectives: Scopolamine (SCO) is widely employed as a pharmacological model of anxiety and amnesia in both rodents and zebrafish, the latter representing a valuable translational model in neuropsychopharmacology. The present study aimed to evaluate the neuroprotective and antioxidant potential of chronic administration of an ethanolic extract from Calendula officinalis flowers (CEE). Methods: Adult zebrafish (n = 10/group, both sexes) were exposed to CEE at concentrations of 1, 3, and 10 mg/L, administered daily for 22 consecutive days. After the initial 7-day pretreatment period, fish were challenged with SCO (100 μM, immersion for 30 min) followed by behavioral testing, including the Novel Tank Diving Test, Light/Dark Test, Novel Approach Test, Y-Maze, and Novel Object Recognition. Subsequently, brain homogenates were analyzed for acetylcholinesterase (AChE) activity, antioxidant enzymes (superoxide dismutase—SOD, catalase—CAT, glutathione peroxidase—GPx), reduced glutathione (GSH), protein carbonyls, and malondialdehyde (MDA). Results: Chronic CEE administration significantly attenuated scopolamine-induced anxiety-like behaviors and improved spatial memory (Y-maze) and recognition memory (NOR), as well as reduced anxiety-like behavior in the SCO-induced zebrafish model. Biochemical analyses revealed that CEE restored AChE activity, enhanced the activity of SOD, CAT, and GPx, and increased GSH levels, while concomitantly reducing protein oxidation and lipid peroxidation. The most pronounced effects were observed at 3 mg/L, which nearly normalized both behavioral and biochemical parameters. Conclusions: The CEE exerted anxiolytic and procognitive effects in zebrafish through combined cholinergic and antioxidant mechanisms. These findings highlight its translational potential as a promising candidate for the prevention and treatment of anxiety-related and cognitive disorders. Full article

Allorecognition, or distinguishing between the self and nonself within the same species, is observed in both animals and plants, particularly in the context of immune reactions and self-incompatibility in sexual reproduction. Polymorphic recognition molecules are known to be responsible for such allorecognition during fertilization. Previous studies have reported that in ascidians and flowering plants, inbreeding avoidance relies on a pair of polymorphic recognition molecules with a receptor-ligand relationship that are encoded at a single locus, the S locus (Self-incompatibility locus), but the process by which such pairs of recognition molecules emerge and evolve to become polymorphic is not known. Here, a population genetics study was carried out as a novel approach for investigating allorecognition. To study the process by which self-recognition emerges, we simulated a situation in which an allorecognizing genotype is generated from a nonallorecognizing genotype through mutation and then analyzed whether the two genotypes could coexist. The conditions under which the numbers of allorecognition alleles could increase over evolutionary time were investigated, and the generational dynamics of nonallorecognizing genotypes were analyzed. Subsequent modeling was carried out to reproduce the allorecognition mechanism in Ciona, and consistency between the simulation results and experimental data was observed. Our approach provides new insight into the evolutionary process of allorecognition. Full article

Cotton is the most important natural fiber crop worldwide, and its automated harvesting is essential for improving production efficiency and economic benefits. However, cotton boll detection faces challenges such as small target size, fine-grained category differences, and complex background interference. This study proposes RA-CottNet, a high-precision object detection model with both directional awareness and attention-guided capabilities, and develops an open-source dataset containing 4966 annotated images. Based on YOLOv11n, RA-CottNet incorporates ODConv and SPDConv to enhance directional and spatial representation, while integrating CoordAttention, an improved GAM, and LSKA to improve feature extraction. Experimental results showed that RA-CottNet achieves 93.683% $Precision$, 86.040% $Recall$, 93.496% $mAP50$, 72.857% $mAP95$, and 89.692% $F1$-$score$, maintaining stable performance under multi-scale and rotation perturbations. The proposed approach demonstrated high accuracy and real-time capability, making it suitable for deployment on agricultural edge devices and providing effective technical support for automated cotton boll harvesting and yield estimation. Full article

Peony flowers in Luoyang are renowned for their diverse varieties and substantial economic value. However, recognizing multiple peony varieties in natural field conditions remains challenging due to limited datasets and the shortcomings of existing detection models. High intra-class similarity among peony varieties, frequent occlusions, and imbalanced sample quality pose significant challenges to conventional approaches. To address these issues, we propose BCP-YOLOv5, an enhanced YOLOv5-based model designed for peony variety detection. The proposed model incorporates the Vision Transformer with Bi-Level Routing Attention (Biformer) to improve the detection accuracy of occluded targets. Inspired by Focal-EIoU, we redesign the loss function as Focal-CIoU to reduce the impact of low-quality samples and enhance bounding box localization. Additionally, Content-Aware Reassembly of Features (CARAFE) is employed to replace traditional upsampling, further improving performance. The experiments show that BCP-YOLOv5 improves precision by 3.4%, recall by 4.4%, and mAP@0.5 by 4.5% over baseline YOLOv5s. This work fills the gap in multi-variety peony detection and offers a practical, reproducible solution for intelligent agriculture. Full article

Understanding and monitoring the phenological phases of trees is essential for ecological research and climate change studies. In this work, we present a comprehensive evaluation of state-of-the-art convolutional neural networks (CNNs) and transformer architectures for the automated classification of the flowering phase of Tilia cordata Mill. (small-leaved lime) based on a large set of real-world images acquired under natural field conditions. The study introduces a novel, automated image quality filtering approach using an XGBoost classifier trained on diverse exposure and sharpness features to ensure robust input data for subsequent deep learning models. Seven modern neural network architectures, including VGG16, ResNet50, EfficientNetB3, MobileNetV3 Large, ConvNeXt Tiny, Vision Transformer (ViT-B/16), and Swin Transformer Tiny, were fine-tuned and evaluated under a rigorous cross-validation protocol. All models achieved excellent performance, with cross-validated F1-scores exceeding 0.97 and balanced accuracy up to 0.993. The best results were obtained for ResNet50 and ConvNeXt Tiny (F1-score: 0.9879 ± 0.0077 and 0.9860 ± 0.0073, balanced accuracy: 0.9922 ± 0.0054 and 0.9927 ± 0.0042, respectively), indicating outstanding sensitivity and specificity for both flowering and non-flowering classes. Classical CNNs (VGG16, ResNet50, and ConvNeXt Tiny) demonstrated slightly superior robustness compared to transformer-based models, though all architectures maintained high generalization and minimal variance across folds. The integrated quality assessment and classification pipeline enables scalable, high-throughput monitoring of flowering phases in natural environments. The proposed methodology is adaptable to other plant species and locations, supporting future ecological monitoring and climate studies. Our key contributions are as follows: (i) introducing an automated exposure-quality filtering stage for field imagery; (ii) publishing a curated, season-long dataset of Tilia cordata images; and (iii) providing the first systematic cross-validated benchmark that contrasts classical CNNs with transformer architectures for phenological phase recognition. Full article

The advancement of Artificial Intelligence (AI) has significantly accelerated progress across various research domains, with growing interest in plant science due to its substantial economic potential. However, the integration of AI with digital vegetation analysis remains underexplored, largely due to the absence of large-scale, real-world plant datasets, which are crucial for advancing this field. To address this gap, we introduce the PP3D dataset—a meticulously labeled collection of about 500 potted plants represented as 3D point clouds, featuring fine-grained annotations for approximately 20 species. The PP3D dataset provides 3D phenotypic data for about 20 plant species spanning model organisms (e.g., Arabidopsis thaliana), potted plants (e.g., Foliage plants, Flowering plants), and horticultural plants (e.g., Solanum lycopersicum), covering most of the common important plant species. Leveraging this dataset, we propose the panoptic plant recognition task, which combines semantic segmentation (stems and leaves) with leaf instance segmentation. To tackle this challenge, we present SCNet, a novel dual-representation learning network designed specifically for plant point cloud segmentation. SCNet integrates two key branches: a cylindrical feature extraction branch for robust spatial encoding and a sequential slice feature extraction branch for detailed structural analysis. By efficiently propagating features between these representations, SCNet achieves superior flexibility and computational efficiency, establishing a new baseline for panoptic plant recognition and paving the way for future AI-driven research in plant science. Full article

The improvement in yield in cultivar mixtures has been well established. Despite increasing knowledge of the improvement involving within-species diversification and resource use efficiency, little is known about the benefits arising from relatedness-mediated intraspecific interactions in cultivar mixtures. This study used a relatedness gradient of rice cultivars to test whether neighbor relatedness contributes to improvements in grain yields in cultivar mixtures. We experimentally demonstrated the grain yield of rice cultivar mixtures with varying genetic relatedness under both field and controlled conditions. As a result, a closely related cultivar mixture had increased grain yield compared to monoculture and distantly related mixtures by optimizing the root-to-shoot ratio and accelerating flowering. The benefits over monoculture were most pronounced when compared to the significant yield reductions observed in distantly related mixtures. The relatedness-mediated improvement in yields depended on soil volume and nitrogen use level, with effects attenuating under larger soil volumes or nitrogen deficiency. Furthermore, neighbor relatedness enhanced the richness and diversity of both bacterial and fungal communities in the rhizosphere soil, leading to a significant restructuring of the microbial community composition. These findings suggest that neighbor relatedness may improve the grain yield of rice cultivar mixtures. Beneficial plant–plant interactions may be generated by manipulating cultivar kinship within a crop species. A thorough understanding of kinship strategies in cultivar mixtures offers promising prospects for increasing crop production. Full article

Wild edible fruit plants are integral to the cultural, nutritional, medicinal, and economic practices of Indigenous Isan communities in Roi Et Province, northeastern Thailand, a region characterized by plateau and lowland topography and a tropical monsoon climate. This study aimed to document the diversity, traditional uses, phenology, and conservation status of these species to inform sustainable management and conservation efforts. Field surveys and ethnobotanical interviews with 200 informants (100 men, 100 women; random ages) were conducted across 20 local communities to identify species diversity and usage patterns, while phenological observations and conservation assessments were performed to understand reproductive cycles and species vulnerability between January and December 2023. A total of 68 species from 32 families were recorded, with peak flowering in March–April and fruiting in May–June. Analyses of Species Use Value (0.19–0.48) and Relative Frequency of Citation (0.15–0.44) identified key species with significant roles in food security and traditional medicine. Uvaria rufa had the highest SUV (0.48) and RFC (0.44). Informant consensus on medicinal applications was strong for ailments such as gastrointestinal and lymphatic disorders. Economically important species were also identified, with some contributing notable income through local trade. Conservation proposed one species as Critically Endangered and several others as Vulnerable. The results highlight the need for integrated conservation strategies, including community-based initiatives and recognition of Other Effective area-based Conservation Measures (OECMs), to ensure the preservation of biodiversity, traditional knowledge, and local livelihoods. Full article

Early crop yield prediction is a major challenge in precision agriculture, and efficient and rapid yield prediction is highly important for sustainable fruit production. The accurate detection of major fruit characteristics, including flowering, green fruiting, and ripening stages, is crucial for early yield estimation. Currently, most crop yield estimation studies based on the YOLO model are only conducted during a single stage of maturity. Combining multi-growth period data for crop analysis is of great significance for crop growth detection and early yield estimation. In this study, a new network model, YOLOv8-RL, was proposed using citrus multigrowth period characteristics as a data source. A citrus yield estimation model was constructed and validated by combining network identification counts with manual field counts. Compared with YOLOv8, the number of parameters of the improved network is reduced by 50.7%, the number of floating-point operations is decreased by 49.4%, and the size of the model is only 3.2 MB. In the test set, the average recognition rate of citrus flowers, green fruits, and orange fruits was 95.6%, the mAP@.5 was 94.6%, the FPS value was 123.1, and the inference time was only 2.3 milliseconds. This provides a reference for the design of lightweight networks and offers the possibility of deployment on embedded devices with limited computational resources. The two estimation models constructed on the basis of the new network had coefficients of determination R² values of 0.91992 and 0.95639, respectively, with a prediction error rate of 6.96% for citrus green fruits and an average error rate of 3.71% for orange fruits. Compared with network counting, the yield estimation model had a low error rate and high accuracy, which provided a theoretical basis and technical support for the early prediction of fruit yield in complex environments. Full article

With the increasing application of hyperspectral technology, rapid and accurate monitoring of cotton leaf nitrogen concentrations (LNCs) has become an effective tool for large-scale areas. This study used Tahe No. 2 cotton seeds with four nitrogen levels (0, 200, 350, 500 kg ha⁻¹) and four phosphorus levels (0, 100, 200, 300 kg ha⁻¹). Spectral data were acquired using an ASD FieldSpec HandHeld2 portable spectrometer, which measures spectral reflectance covering a band of 325–1075 nm with a spectral resolution of 1 nm. LNCs determination and spectral estimation were conducted at six growth stages: squaring, initial bloom, peak bloom, initial boll, peak boll, and boll opening. Thirty-seven spectral indices (SIs) were selected. First derivative (FD), standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay (SG) were applied to preprocess the spectra. Feature bands were screened using partial least squares discriminant analysis (PLS–DA), and support vector machine (SVM) and random forest (RF) models were used for accuracy validation. The results revealed that (1) LNCs initially increased and then decreased with growth, peaking at the full-flowering stage before gradually declining. (2) The best LNC recognition models were SVM–MSC in the squaring stage, SVM–FD in the initial bloom stage, SVM–FD in the peak bloom stage, SVM–FD in the initial boll stage, RF–SNV in the peak boll Mstage, and SVM–FD in the boll opening stage. FD showed the best performance compared with the other three treatments, with SVM outperforming RF in terms of higher R² and lower RMSE values. The SVM–FD model effectively improved the accuracy and robustness of LNCs prediction using hyperspectral leaf spectra, providing valuable guidance for large-scale information production in high-standard cotton fields. Full article

The establishment of conservation areas is considered one of the most effective approaches to address biodiversity loss with limited resources. Identifying hotspots of plant diversity and conservation gaps has played a crucial role in optimizing conservation areas. Utilizing diverse types of research data can effectively enhance the recognition of hotspots and conservation gaps. Phenotypic trait diversity is a functional biogeography that analyzes the geographic distribution patterns, formation, and reasons for the development of specific or multiple phenotypic traits of organisms. Flower color and fruit color phenotypic traits are primary characteristics through which plants interact with other organisms, affecting their own survival and reproduction, and that of their offspring. This study utilized data from 1923 Phenotypic Trait Diversity Species (PTDS) with flower and fruit color characteristics to optimize conservation areas in the Shaanxi Qinling Mountains. Additionally, data from 1838 endemic species (ES), 190 threatened species (TS), and 119 protected species (PS) were used for validation. The data were primarily sourced from the Catalogue of Vascular Plants in Shaanxi, supplemented by the Chinese Virtual Herbarium and the Shaanxi Digital Herbarium. The results reveal that by comparing the existing conservation area boundaries with those determined by four types of data, conservation gaps are found in 14 counties in the Qinling Mountains of Shaanxi. The existing conservation area only accounts for 13.3% of the area determined by the four types of data. There are gaps in biodiversity conservation in the Qinling Mountains of Shaanxi, and the macroscopic use of plant phenotypic trait data contributes to optimizing these conservation gaps. Full article

Edible flowers are gaining recognition as rich sources of nutrients and phytochemicals. In Mexico, the flower of Agave inaequidens has been traditionally consumed since pre-Hispanic times. This study investigated its nutritional profile and the in vitro gastrointestinal bioaccessibility of its phenolic fraction. During in vitro digestion (oral, gastric, and intestinal), the total phenolic content of A. inaequidens significantly decreased from 138 to 21 mg GAE/100 g DW (15.22% bioaccessibility), while total flavonoid content dropped from 8 to 4.6 mg CE/100 g DW (57.5% bioaccessibility). Consequently, antioxidant activity, assessed by ABTS, DPPH, and hemolysis inhibition assays, also declined post-digestion. Interestingly, the digestive process modulated the flower’s inhibitory activity against digestive enzymes before and after in vitro digestion: α-amylase inhibition slightly decreased (IC₅₀ 1.8 to 2.1 mg/mL), but α-glucosidase (IC₅₀ 2.7 to 1.6 mg/mL) and lipase (IC₅₀ > 3 to 1.4 mg/mL) inhibition increased. The A. inaequidens flower is a good source of fiber and low in fat. These findings underscore its potential as a functional food ingredient, offering bioaccessible phenolic compounds with antioxidant and enzyme inhibitory properties. Full article

Honeybee olfaction can influence foraging behavior and affect crop pollination. Odor-binding proteins play a vital role in honeybee olfactory perception. A previous study based on the antennal transcriptome of Apis mellifera ligustica in melon and tomato greenhouses revealed that AmelOBP2 is highly expressed. Therefore, we aimed to further investigate the olfactory recognition mechanism of honeybees by detecting the expression levels and binding ability of AmelOBP2 to floral volatiles of melon and tomato flowers. The results show that AmelOBP2 mRNA was highly expressed in the antennae of honeybees, and its protein expression was highest in the antennae at 20 days of age and was higher in the melon greenhouse. The binding ability of AmelOBP2 to floral volatiles of melon was stronger than that of tomato. AmelOBP2 had a stronger binding ability with aldehydes in melon floral volatiles and with terpenes and benzenes in tomato floral volatiles. After feeding with siRNA, the electroantennogram response of honeybees to E-2-hexenal, E-2-octenal, and 1-nonanal decreased markedly, confirming the role of AmelOBP2 in the recognition of melon and tomato floral volatiles. These results elucidate the molecular mechanisms underlying honeybee flower-visiting behavior and provide a theoretical reference for regulating the behavior of honeybees using plant volatiles. Full article

Background/Objectives: Sleep is essential to human health, playing a vital role in physical and mental well-being. Sleep disorders can lead to significant health complications, such as cardiovascular problems, diabetes, obesity, and depression. In Brazil, plants such as passionflower (Passiflora spp.), chamomile (Matricaria chamomilla L.) and mulungu (Erythrina spp.) are widely used in folk medicine for their sleep-promoting properties. This article reviews the existing literature on the sleep-promoting effects of these plants, focusing on the Brazilian context and popular knowledge of their use. Methods: An integrative literature review was conducted, including scientific articles in English and Portuguese from PubMed, Scielo and Google Scholar databases. Ethnobotanical studies documenting the traditional use of these plants in Brazil and clinical and preclinical research on their sleep-promoting effects were included. Results: The juice and infusion of the leaves and fruits of passionflower are mainly used to treat anxiety and insomnia, chamomile flower tea is used for its sedative effects, and mulungu bark decoctions are used for their sedative and anxiolytic properties. These popular uses are supported by scientific studies demonstrating the efficacy of these plants in treating insomnia, anxiety, and stress. Conclusions: The recognition of traditional knowledge and the inclusion of these plants in RENISUS highlights their importance for public health in Brazil. However, more rigorous clinical trials are needed to confirm their efficacy and safety and ensure their safe integration into modern medicine. Full article