Abstract C 4 and CAM photosynthesis evolved repeatedly across angiosperms, yet the four palm species examined here — representing three of five Arecaceae subfamilies (∼2,600 species total) — all lack both. We hypothesized that this reflects the absence of PEPC1, a phosphoenolpyruvate carboxylase isoform required for C 4 carbon fixation initiation and present in commelinids (Poaceae + Bromeliaceae) but whose distribution across monocots is unknown. We surveyed six core C 4 enzyme families by HMMER profiling across nine plant genomes — four palms (Cocos nucifera, Elaeis guineensis, Phoenix dactylifera, Nypa fruticans), two grasses, one bromeliad, one basal monocot, and one fern. We constructed maximum-likelihood PEPC phylogenies and performed codon-based branch-site likelihood ratio tests using PAML. All six enzyme families were present in palms. PEPC1 was detected in all commelinids (rice, maize, pineapple) but absent from all four palm species and outgroups. No palm PEPC sequence fell within the PPC-1 clade. Molecular dating indicates the Arecaceae–commelinid divergence (∼120 Mya) predates PEPC1 origin (∼105 Mya). Branch-site tests on the maize C 4 -PEPC1 branch were marginally significant (2Δ l = 3.03, p ≈ 0.082). The absence of PEPC1 represents an ancestral molecular barrier that likely precluded C 4 evolution in palms. This identifies a lineage-specific gene duplication as a constraint on photosynthetic pathway evolution in a major tropical plant lineage.

Coconut is the fruit of the coconut palm. Due to its characteristics of a long growth cycle and low germination rate, accurate prediction of its developmental status is particularly important. Traditional research primarily relies on the sectioning method to observe its internal structure. Although this approach can reveal morphological characteristics, its destructive nature prevents continuous monitoring of the internal developmental processes. Recent advancements in Computed Tomography (CT)-based nondestructive imaging and artificial intelligence have enabled novel approaches for investigating internal coconut morphology. However, current methodologies frequently overlook the impact of field environmental factors on coconut germination processes, consequently constraining prediction accuracy. To address this issue, this study proposes a Transformer-based multimodal feature fusion predictive model. Through the integration of CT images and environmental data, the model achieves precise prediction of coconut developmental status. Initially, the enhanced Deeplab V3+ model extracts deep semantic features from coconut CT images, while Fourier positional encoding is applied to amplify periodic features in environmental data (e.g., temperature, humidity). Subsequently, a cross-modal multi-head attention mechanism is designed to achieve comprehensive fusion between CT-derived semantic features and field data characteristics, thoroughly exploring their correlations. Ultimately, to further enhance model performance, this study incorporates supervised contrastive loss functions and implements intra-class feature aggregation coupled with inter-class feature separation strategies for feature space optimization. The experimental results demonstrate that the proposed model achieves superior performance in coconut developmental stage prediction tasks: compared with conventional unimodal approaches, it improves prediction accuracy and F1-score by 9 % and 8 %, respectively, thereby validating the effectiveness of multimodal data fusion and the rationality of the model design.

Coconut shows unique physiological characteristics and application value at different developmental stages, and accurate identification and classification of its internal developmental stages are important for research and planting management. With the rapid development of CT nondestructive testing and artificial intelligence technology, developmental monitoring based on the internal morphological characteristics of coconuts has become possible. However, limited by the limited number of coconut CT samples, the recognition accuracy of existing classification models for internal developmental stages is significantly restricted. In this study, a method for classifying multiple developmental stages of coconuts based on few-shot image generation is proposed. Firstly, an improved few-shot image generation model FastGAN-Pro is proposed, which is capable of generating higher-quality CT images of coconuts at different developmental stages with a small amount of training data. On this basis, a multi-developmental stage classification method based on migration learning is proposed based on two pre-trained models, VGG16 and ResNet18. The experimental results show that compared with the baseline model, the FID values of FastGAN-Pro are reduced by about 21.08% on average, and the generated images are more visually.

Oil palm (Elaeis guineensis Jacq.) plays a vital role in the global food system, yet questions about its nutrition, safety, and sustainability continue to spark debate. This review takes a fresh, integrative approach, connecting what we know about nutrition, processing innovations, and sustainability governance within a single, systems-level framework. Palm oil is a rich source of energy and beneficial compounds like tocotrienols, carotenoids, and phytosterols, with research increasingly showing that its health impact depends on how it is consumed and processed. Advances in refining such as enzymatic degumming, low-temperature deodorization, and structured lipid design have significantly reduced harmful contaminants like 3-MCPD and glycidyl esters, sometimes by as much as 80%. On the sustainability side, challenges like deforestation, peatland conversion, and greenhouse gas emissions persist, even as certification programs (RSPO, MSPO, ISPO) and NDPE commitments make some progress. By weaving together these nutritional, technological, and environmental perspectives, this review offers a holistic roadmap for aligning palm oil production with global food security, climate action, and public health goals, providing actionable insights for researchers, policymakers, and industry leaders.

Oil palm (Elaeis guineensis Jacq.) is a quintessential tropical oilseed crop, whose distribution and yield are significantly constrained by low-temperature stress. This study conducted physiological and proteomic analyses to investigate the early response mechanisms of one-year-old thin-shelled oil palm seedlings exposed to low temperatures of 8 °C for durations of 0, 0.5, 1, 2, 4, and 8 h. The results indicated that low-temperature treatment inhibited chlorophyll accumulation, reduced photosynthetic efficiency, and increased the levels of malondialdehyde (MDA) and soluble sugars. Concurrently, the activities of antioxidant enzymes exhibited a transient increase, suggesting an imbalance in the antioxidant defense system during the later stages of stress. Proteomics results indicate that the core pathways involved are fatty acid degradation and the regulatory pathways for starch and sucrose metabolism. Within these pathways, key proteins such as ACSL (long-chain fatty acid-CoA ligase), paaF (enoyl-CoA hydratase), HADH (3-hydroxyacyl-CoA dehydrogenase), and HADHA (enoyl-CoA hydratase/long-chain 3-hydroxyacyl-CoA dehydrogenase) are identified among 13 differential accumulation protein (DAPs). It is hypothesized that these key proteins work in concert to regulate osmosis, scavenge reactive oxygen species (ROS), stabilize membranes, and supply energy. Through physiological and proteomic analyses, this study identified physiological indicators, key proteins, and regulatory pathways associated with cold tolerance in oil palm, thus providing a theoretical basis for breeding low-temperature-tolerant oil palm varieties. The findings contribute to the development of low-temperature-tolerant oil palm cultivars.

Abstract Perennial crops follow different domestication trajectories from annuals, yet the molecular basis of slow-variable domestication—subtle tuning of conserved regulatory hubs—remains poorly characterized. We reconstructed the evolutionary history of the chloroplast kinase ABC1K7 across 9 seed plant species spanning ∼350 Myr, employing PAML codon models, IQ-TREE robust codon models, and protein-level phylogenetic inference, with AlphaFold2 structural modeling. ABC1K7 was under extreme purifying selection (ω = 0.073–0.104) across all seed plants. In coconut, a single Y→F substitution at residue 652—located >30 Å from the catalytic core in a predicted intrinsically disordered region—represents the only non-synonymous change differentiating coconut from 7 of 8 angiosperm orthologs, and exhibits perfect co-segregation with domestication traits across a 17-year breeding panel ( n = 327). These findings provide population-level evidence consistent with the slow-variable domestication model, identifying ABC1K orthologs as targets for perennial crop improvement.

In this study, Wenchang Sweet Coconut (Cocos nucifera L. 'Wenchang-Sweet') was selected as the experimental material, with Hainan Tall Coconut (Cocos nucifera L. 'Hainan-Tall') water served as the control. Changes in key secondary metabolites-includingphenolic acids, alkaloids, terpenoids, lignans, and coumarins-in coconut water were analyzed across six maturity stages (I to VI, corresponding to 2, 4, 6, 8, 10, and 12 months of age). Metabolite profiling and differential screening were performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with a metabolomics approach. The results showed that 168 secondary metabolites were identified in the coconut water of both HTC and WSC, comprising 120 phenolic acids, 26 alkaloids, 6 terpenoids, 12 lignins and 4 coumarins. During the ripening process, the content of secondary metabolite in the coconut water declined in of both HTC and WSC coconuts, however, WSC consistently exhibited significantly higher levels. The number of differentially expressed secondary metabolites between WSC and HTC at each ripening stage was 78, 47, 54, 68, 80, and 62, respectively. Key metabolites such as Choline, Chlorogenic acid and Caffeic acid were identified, along with 30 characteristic secondary metabolites. KEGG pathway enrichment analysis revealed that secondary metabolites were primarily enriched in eight metabolic pathways including aminobenzoate degradation (ko00627), phenylpropanoid biosynthesis (ko00940) and biosynthesis of various plant secondary metabolites (ko00999). This study provides a strong theoretical foundation for functional research and product development related to coconut secondary metabolites.

Due to the unique structure of the coconut, the internal changes in the early process of coconut germination cannot be observed without damage. With the development of imaging technology, computed tomography (CT) has gradually become a non-invasive tool to observe the internal structure of plants. In this study, the growth of haustorium, bud and roots, as well as the density and morphological changes of the internal structure of 100 coconuts from picking to germination were noninvasive observed based on CT imaging for 15 months, to explore the internal development process of coconuts and the factors affecting the planting survival. The results showed that the minor axis of coconut and major axis of coconut increased with time, and the thickness and density of mesocarp and endocarp, weight and liquid endosperm volume decreased with time. Haustorium first appeared in coconut, then appeared bud, and finally formed roots. Mesocarp thickness, endocarp thickness and the presence of haustorium, bud and roots can be used as key factors to predict the survival of coconut. The predictive performance was obtained with an AUC of 0.818 (95 %CI: 0.724-0.912) by binary regression modeling based on the above factors. The CT images can help people understand the internal changes in the process of coconut germination and guide the process of coconut cultivation.

With the continuous progress of technology, computed tomography (CT) technology has expanded from medicine to agriculture and other industries. With the advantages of non-destructiveness, high resolution, and high precision, CT technology shows great application potential in the agricultural field. However, there are still some problems with this technology that need to be solved. This paper aims to show the application of CT technology in the agricultural field, find technical challenges, and put forward specific countermeasures, so that CT technology can be better applied in the agricultural field. This paper summarizes the application of CT technology in the quality detection of agricultural products, disease and insect pest identification, seed screening, soil analysis, and precision agriculture management, and focuses on the current challenges and the countermeasures, and looks into the role of this technology in promoting agricultural development in the future. Despite various challenges, CT technology has far more advantages than disadvantages, and it is expected to become an indispensable part of all the links of agricultural production and promote the development of precision agriculture and smart agriculture.

Lignans play a crucial role in maintaining plant growth, development, metabolism and stress resistance. Computed tomography (CT) imaging technology can be used to explore the internal structure and morphology of plants, and understanding the correlation between the two is highly significant. In this study, the content of lignan metabolites in coconut water was determined using liquid chromatography. The internal structure data of coconut fruit was obtained by CT scanning, and the relationship between lignan metabolites and CT image data at different developmental stages was evaluated using partial least square (PLS) regression. The results showed that the total lignan content in coconut water initially decreased, then increased, and gradually decreased after the maturity stage. The Wenye No. 5 variety exhibited higher levels of Epiturinol, Turbinol, Isobarinin-9'-o-glucoside, 5'-methoxy-rohanoside, Rohan rosin-4,4'-di-o-glucoside, turbinol-4-O-glucoside, cycloisoperinolin-4-O-glucoside compared to local coconuts. Coconut meat had the greatest effect on Rohan rosin-4,4'-di-o-glucoside, coconut water on Daphne, and coconut shell and coconut fiber on Larinin-4'-o-glucoside. The data from different parts of coconut fruit's images showed a significant correlation with the content of lignan metabolites. This study has preliminarily explored the correlation between non-destructive testing of coconut fruit and its development process of coconut fruit, providing a new approach and method for further research on non-destructive testing of coconut fruit development.

Background The study aimed to observe the internal structure of coconuts from two locations (coastal and non-coastal) using computed tomography (CT). Methods Seventy-six mature coconuts were collected from Wenchang and Ding’an cities in Hainan Province. These coconuts were scanned four times using CT, with a two-week interval between each scan. CT data were post-processed to reconstruct two-dimensional slices and three-dimensional models. The density and morphological parameters of coconut structures were measured, and the differences in these characteristics between the two groups and the changes over time were analyzed. Results Time and location had interactive effects on CT values of embryos, solid endosperms and mesocarps, morphological information such as major axis of coconut, thickness of mesocarp, volume of coconut water and height of bud (p < 0.05). Conclusions Planting location and observation time can affect the density and morphology of some coconut structures.

Coconut meat and coconut water have garnered significant attention for their richness in healthful flavonoids. However, the dynamics of flavonoid metabolites in coconut water during different developmental stages remain poorly understood. This study employed the metabolomics approach using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate the changes in flavonoid metabolite profiles in coconut water from two varieties, ‘Wenye No.5’(W5) and Hainan local coconut (CK), across six developmental stages. The results showed that a total of 123 flavonoid metabolites including chalcones, dihydroflavonoids, dihydroflavonols, flavonoids, flavonols, flavonoid carboglycosides, and flavanols were identified in the coconut water as compared to the control. The total flavonoid content in both types of coconut water exhibited a decreasing trend with developmental progression, but the total flavonoid content in CK was significantly higher than that in W5. The number of flavonoid metabolites that differed significantly between the W5 and CK groups at different developmental stages were 74, 74, 60, 92, 40 and 54, respectively. KEGG pathway analysis revealed 38 differential metabolites involved in key pathways for flavonoid biosynthesis and secondary metabolite biosynthesis. This study provides new insights into the dynamics of flavonoid metabolites in coconut water and highlights the potential for selecting and breeding high-quality coconuts with enhanced flavonoid content. The findings have implications for the development of coconut-based products with improved nutritional and functional properties.

Due to the unique structure of coconuts, their cultivation heavily relies on manual experience, making it difficult to accurately and timely observe their internal characteristics. This limitation severely hinders the optimization of coconut breeding. To address this issue, we propose a new model based on the improved architecture of Deeplab V3+. We replace the original ASPP(Atrous Spatial Pyramid Pooling) structure with a dense atrous spatial pyramid pooling module and introduce CBAM(Convolutional Block Attention Module). This approach resolves the issue of information loss due to sparse sampling and effectively captures global features. Additionally, we embed a RRM(residual refinement module) after the output level of the decoder to optimize boundary information between organs. Multiple model comparisons and ablation experiments are conducted, demonstrating that the improved segmentation algorithm achieves higher accuracy when dealing with diverse coconut organ CT(Computed Tomography) images. Our work provides a new solution for accurately segmenting internal coconut organs, which facilitates scientific decision-making for coconut researchers at different stages of growth.

Coconut is one of the largest seeded fruits in the world. While its hard shell provides some protection for the growth and development of coconuts, it also hinders the observation of internal structural changes and quality inspection during early seedling selection. The cultivation of coconut seedlings takes about a year, and it takes several years for them to grow into mature coconut trees. Therefore, early selection of coconuts is crucial. To address the issue of difficult observation of internal coconut features, we utilized CT scans for non-destructive scanning to obtain internal images of coconuts at different stages. We annotated the internal structures, such as coconut water, haustorium, germ and internal cracks, which exhibit significant changes during coconut growth and development. This led to the establishment of a dataset called Internal Structure Feature of Coconuts (ISFC), providing data resources for coconut image detection. In this paper, we propose an improved model based on YOLOv7 object detection specifically designed for the ISFC dataset, aiming to detect notable changes in internal coconut features and cracks. Experimental results demonstrate that this method accurately identifies and detects the targets, achieving an average precision of up to 97.4%. It satisfies the requirements for high-precision detection and provides a basis for observing internal coconut features and conducting quality inspections during development.

Background As one of the largest drupes in the world, the coconut has a special multilayered structure and a seed development process that is not yet fully understood. On the one hand, the special structure of the coconut pericarp prevents the development of external damage to the coconut fruit, and on the other hand, the thickness of the coconut shell makes it difficult to observe the development of bacteria inside it. In addition, coconut takes about 1 year to progress from pollination to maturity. During the long development process, coconut development is vulnerable to natural disasters, cold waves, typhoons, etc. Therefore, nondestructive observation of the internal development process remains a highly important and challenging task. In this study, We proposed an intelligent system for building a three-dimensional (3D) quantitative imaging model of coconut fruit using Computed Tomography (CT) images. Cross-sectional images of coconut fruit were obtained by spiral CT scanning. Then a point cloud model was built by extracting 3D coordinate data and RGB values. The point cloud model was denoised using the cluster denoising method. Finally, a 3D quantitative model of a coconut fruit was established. Results The innovations of this work are as follows. 1) Using CT scans, we obtained a total of 37,950 non-destructive internal growth change maps of various types of coconuts to establish a coconut data set called “CCID”, which provides powerful graphical data support for coconut research. 2) Based on this data set, we built a coconut intelligence system. By inputting a batch of coconut images into a 3D point cloud map, the internal structure information can be ascertained, the entire contour can be drawn and rendered according to need, and the long diameter, short diameter and volume of the required structure can be obtained. We maintained quantitative observation on a batch of local Hainan coconuts for more than 3 months. With 40 coconuts as test cases, the high accuracy of the model generated by the system is proven. The system has a good application value and broad popularization prospects in the cultivation and optimization of coconut fruit. Conclusion The evaluation results show that the 3D quantitative imaging model has high accuracy in capturing the internal development process of coconut fruits. The system can effectively assist growers in internal developmental observations and in structural data acquisition from coconut, thus providing decision-making support for improving the cultivation conditions of coconuts.

INTRODUCTION:Computed tomography (CT) is a non-invasive examination tool that is widely used in medicine. In this study, we explored its value in visualizing and quantifying coconut.MATERIALS AND METHODS:Twelve coconuts were scanned using CT for three months. Axial CT images of the coconuts were obtained using a dual-source CT scanner. In postprocessing process, various three-dimensional models were created by volume rendering (VR), and the plane sections of different angles were obtained through multiplanar reformation (MPR). The morphological parameters and the CT values of the exocarp, mesocarp, endocarp, embryo, bud, solid endosperm, liquid endosperm, and coconut apple were measured. The analysis of variances was used for temporal repeated measures and linear and non-linear regressions were used to analyze the relationship between the data.RESULTS:The MPR images and VR models provide excellent visualization of the different structures of the coconut. The statistical results showed that the weight of coconut and liquid endosperm volume decreased significantly during the three months, while the CT value of coconut apple decreased slightly. We observed a complete germination of a coconut, its data showed a significant negative correlation between the CT value of the bud and the liquid endosperm volume (y = -2.6955x + 244.91; R2 = 0.9859), and a strong positive correlation between the height and CT value of the bud (y = 1.9576 ln(x) -2.1655; R2 = 0.9691).CONCLUSION:CT technology can be used for visualization and quantitative analysis of the internal structure of the coconut, and some morphological changes and composition changes of the coconut during the germination process were observed during the three-month experiment. Therefore, CT is a potential tool for analyzing coconuts.

我国椰子种植模式较为单一,单位面积内经济效益低,已不适应现代椰子产业发展的需要.设置三角种植和对照种植椰子两种处理,测定植株田间光照强度,株高、茎围等农艺性状指标,研究三角种植模式对椰子生长的影响.结果表明,三角种植模式对植株光照强度和农艺性状指标有影响,在一定程度上促使椰子向外倾斜生长,除茎围外,株高、叶片数、小叶数、叶长与对照未达到显著性差异水平.说明三角种植模式可在椰子具有趋性生长特性的条件下,提高种植密度,促进单位面积产值潜力.

Because of the special structure of coconut, it is difficult to observe the development of its internal structure accurately, so the research on the development of coconut organs is blank at present, which leads to the quality and quality of coconut can not be guaranteed. To solve this problem, we use CT to carry out non-destructive scanning, to obtain different kinds, different stages of coconut internal image images. In this paper, a novel YOLO V5 model was designed to detect Coconut haustorium and plumule, which are the key structures of Coconut. In order to improve the detection accuracy of coconut haustoriums and buds, an ASFF mechanism and a GAM module were incorporated into YOLO V5 model. The experimental results show that the model can detect the haustoriums and plumules in different periods, and the average precision is improved to 92.43%, which meets the need of high precision detection, thus, it can provide information for intelligent prediction of coconut development for decision making.

对海南省9个县(市)初选的15份椰子优良种质,及对照品种本地椰和文椰3号进行性状评测,包括株高、茎围、花序长度、单果质量、可食率等农艺性状以及总糖、总酸、可溶性固形物、蛋白质、脂肪含量等果实品质性状,并分别进行农艺性状和果实品质性状相关性、主成分分析,筛选优质椰子种质.结果表明,优株15-19与15-17椰水清甜、果皮薄、产量高等方面综合表现良好,是适宜在海南省栽培的矮种鲜食甜水种质.离地0.2 m处茎围、可食率、蒂孔距、雌花数量、花序柄长5个农艺性状及总糖、总酸、可溶性固形物和蛋白质含量4个果实品质性状可以作为甜水椰子快速评价及定向选育的重要指标.

为给水果型椰子苗期的科学施肥管理提供指导,以海南省文椰"3号"叶片为研究对象,采用DRIS指数法,对6个月椰子苗叶片的11种养分(氮、磷、钾、钙、镁、钠、铜、锌、铁、锰、硼)进行营养诊断.结果表明,枯萎椰子苗钾、硼、钙、镁、氮相对缺乏,锌、钠、铜、磷、锰、铁相对充足,椰子苗期(6个月)树体需肥顺序从高到低依次为钾>硼>钙>镁>氮>锌>钠>铜>磷>锰>铁.利用健康椰苗叶片养分浓度制定了相应的养分诊断标准,即各养分的适宜浓度范围为氮含量16.680～20.070 g/kg,磷含量1.170～1.360 g/kg,钾含量6.670～7.780 g/kg,钙含量4.170～4.550 g/kg,钠含量2.780～3.301 g/kg,镁含量2.930～3.470 g/kg,铁含量47.958～52.297 mg/kg,锰含量96.913～119.057 mg/kg,铜含量3.048～3.410 mg/kg,锌含量10.367～12.701 mg/kg,硼含量22.724～27.610 mg/kg.从DRIS法诊断结果来看,椰苗叶片矿质营养元素含量与椰苗"枯萎现象"之间密切相关.