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Chrysanthemum has a long history of medicinal use with rich germplasm resources and extensive cultivation. Traditional chrysanthemum cultivation involves complex patterns and long flowering periods, with the ongoing expansion of planting areas complicating statistical surveys. Currently, reliable, timely, and universally applicable standardized monitoring methods for chrysanthemum cultivation areas remain underdeveloped. This research employed 16 m resolution satellite imagery spanning 2021 to 2023 alongside 2 m resolution data acquired in 2022 to quantify chrysanthemum cultivation extent across Sheyang County, Jiangsu Province, China. After evaluating multiple classifiers, Maximum Likelihood Classification was selected as the optimal method. Subsequently, time-series-based post-classification processing was implemented: initial cultivation information extraction was performed through feature comparison, supervised classification, and temporal analysis. Accuracy validation via Overall Accuracy, Kappa coefficient, Producer’s Accuracy, and User’s Accuracy identified critical issues, followed by targeted refinement of spectrally confused features to obtain precise area estimates. The chrysanthemum cultivation area in 2022 was quantified as 46,950,343 m² for 2 m resolution and 46,332,538 m² for 16 m resolution. Finally, the conversion ratio characteristics between resolutions were analyzed, yielding adjusted results of 38,466,192 m² for 2021 and 47,546,718 m² for 2023, respectively. These outcomes demonstrate strong alignment with local agricultural statistics, confirming method viability for chrysanthemum cultivation area computation. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

The market for plant-based alternatives to animal foods has increased rapidly in the past decade, mainly due to consumer demand. Little evidence is available regarding nutritional impacts, drivers, and barriers to using these products as substitutes for animal foods in real-life conditions. This pilot study followed 16 Danish adults (30 ± 11 years old; 11 females) for 4 weeks with substituting milk, cheese, and yogurt with plant-based analogues to dairy (PBADs) and assessed their drivers and barriers to applying the intervention with a mixed-method approach. PBADs are constantly compared to their animal counterparts, both regarding product characteristics, such as price and sensory properties, as well as cultural roles and subjective memories. The mixed methods showed dairy attachment, price, and taste were the main barriers to consuming PBAD, while changes in life and social circles were drivers (qualitative data). As for the liking of PBADs, plant-based yoghurt was the preferred intervention product (73.5/100, p < 0.05), followed by plant-based drinks (65.9/100), while plant-based cheese was the lowest rated (47.9/100, p < 0.05). As for dietary changes, a lower average intake of sugars, saturated fatty acids, cholesterol, calcium, phosphorus, and zinc was observed after the intervention. Additionally, this study describes the attachment of the study population to milk and dairy products. It shows that choosing dairy is beyond nourishment but is connected to tradition, culture, pleasure, memories, and a sense of belonging. In contrast, there is no history or attachment to PBADs. Full article

Chimonanthus praecox, a traditional ornamental plant in China, is admired for its ability to bloom during the cold winter season and is recognized as an outstanding woody cut flower. MADS-box genes encode transcription factors essential for plant growth and development, with key functions in regulating flowering time and the formation of floral organs. In this study, 74 MADS-box genes (CpMADS1–CpMADS74) were identified and mapped across 11 chromosomes, with chromosome 1 harboring the highest number (13 genes) and chromosome 3 the fewest (3 genes). Physicochemical property analysis revealed that all CpMADS proteins are hydrophilic and predominantly nuclear-localized. Phylogenetic analysis classified these genes into Type I and Type II subfamilies, highlighting a clear divergence in domain structure. Eighty simple sequence repeat (SSR) loci were detected, with dinucleotide repeats being the most abundant, and the majority located in Type II MADS genes. From 23 C. praecox samples, 10 polymorphic SSR markers were successfully developed and PCR-validated, enabling a cluster analysis that grouped these cultivars into three distinct clusters. This study offers significant insights into the regulation of flowering, floral organ development, genetic linkage map construction, and the application of marker-assisted selection in C. praecox. Full article

Black soldier fly frass (BSFF) is a nutrient-rich organic byproduct with growing potential as a sustainable fertilizer. While its effects on crops have been studied, its impact on tree seedling development for reforestation remains poorly understood. This study evaluated the effect of BSFF on the growth and survival of two native Malagasy tree species: the fast-growing Dodonaea madagascariensis and the slow-growing Verpis macrophylla. A six-month nursery experiment tested three BSFF application rates (half-, one-, and two-fold nitrogen equivalence), along with cattle manure, synthetic NPK, and a no-fertilizer control. The survival was highest in the half-fold BSFF (95% for D. madagascariensis, 87.5% for V. macrophylla) and lowest in BSFF two-fold (0% and 22.5%, respectively) treatments. NPK also significantly reduced the survival (5% for D. madagascariensis, 17.5% for V. macrophylla). The growth responses were most pronounced in D. madagascariensis, where the BSFF half- and one-fold treatments led to height growth rates that were 2.0–2.7 times higher than that of the control, cattle manure, and NPK treatments, and diameter growth that was 1.8–2.3 times higher. The biomass accumulation was also significantly higher under the BSFF half- and one-fold treatments for D. madagascariensis. In contrast, V. macrophylla showed limited response to the treatments. These findings indicate that calibrated BSFF application can enhance seedling performance in reforestation efforts, particularly for fast-growing species. Notably, the growth rate of D. madagascariensis doubled (in terms of cm/month) under optimal BSFF treatment—a critical advantage, as time is a key constraint in reforestation and faster growth directly supports more efficient forest restoration. This highlights BSFF’s potential as a sustainable and locally available input for forest restoration in Madagascar. Full article

Forest ginseng (FG) is a rare medicinal and culinary plant in China, and its drying quality is heavily dependent on the drying method. This study investigated the effects of traditional hot air drying (HAD) and the self-developed negative-pressure circulating airflow-assisted desiccator drying (PCAD) method on the quality of FG using metabolomics and enzyme activity. The results revealed that the enzyme activities of dried FG were reduced considerably. PCAD preserved higher enzyme activity than HAD. Metabolomics data demonstrate that HAD promotes the formation of primary metabolites (amino acids, lipids, nucleotides, etc.), whereas PCAD promotes the formation of secondary metabolites (terpenoids, phenolic acids, etc.). A change-transformation network was built by combining the metabolites listed above and their biosynthetic pathways, and it was discovered that these biosynthetic pathways were primarily associated with the mevalonate (MVA) pathway, lipid metabolism, phenylpropane biosynthesis, and nucleotide metabolism. It is also believed that these findings are related to the chemical stimulation induced by thermal degradation and the ongoing catalysis of enzyme responses to drought stress. The facts presented above will give a scientific basis for the selection of FG drying processes, as well as helpful references for increasing the nutritional quality of processed FG. Full article

Adulteration detection in medicinal plant powders remains a critical challenge in quality control. In this study, we propose a hyperspectral imaging (HSI)-based method combined with deep learning models to quantitatively analyze adulteration levels in Anoectochilus roxburghii powder. After preprocessing the spectral data using raw, first-order, and second-order Savitzky–Golay derivatives, we systematically evaluated the performance of traditional machine learning models (Random Forest, Support Vector Regression, Partial Least Squares Regression) and deep learning architectures. While traditional models achieved reasonable accuracy (R² up to 0.885), their performance was limited by feature extraction and generalization ability. A single-channel convolutional neural network (CNN) utilizing individual spectral representations improved performance marginally (maximum R² = 0.882), but still failed to fully capture the multi-scale spectral features. To overcome this, we developed a multi-channel CNN that simultaneously integrates raw, SG-1, and SG-2 spectra, effectively leveraging complementary spectral information. This architecture achieved a significantly higher prediction accuracy (R² = 0.964, MSE = 0.005), demonstrating superior robustness and generalization. The findings highlight the potential of multi-channel deep learning models in enhancing quantitative adulteration detection and ensuring the authenticity of herbal products. Full article

Background/Objectives: Possessing red and white ecotypes, and utilized in traditional Guyanese medicine, Doliocarpus dentatus’ red ecotype is preferred locally for its purported superior therapeutic efficacy. Although therapeutic metabolites were detected in D. dentatus previously, phytohormones remain largely unexplored, until now. Cytokinins, phytohormones responsible for plant cell division, growth and differentiation, are gaining traction for their therapeutic potential in human health. This study screened and quantified endogenous cytokinins and correlated detected cytokinins with selected secondary metabolites. Methods: Liquid chromatography–mass spectrometry was used to acquire phytohormone and metabolite data. Bioinformatics tools were used to assess untargeted metabolomics datasets via statistical and pathway analyses, and chemical groupings of putative metabolites. Results: In total, 20 of the 35 phytohormones were detected and quantified in both ecotypes, with the red ecotype displaying higher free base and glucoside cytokinin concentrations and exhibited 6.2 times the total CK content when compared to the white ecotype. Pathway analysis revealed flavonoid and monoterpenoid biosynthesis in red and white ecotypes, respectively. Positive correlations between specific cytokinins and alkaloids, and between trans-Zeatin and isopentenyladenosine riboside with phenolic compounds were observed. Conclusions: These results suggest that the red ecotype’s elevated cytokinin levels coupled with flavonoid biosynthesis enrichment support its preference in Guyanese traditional medicine. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Skin disorders are common and often chronic conditions with significant therapeutic challenges. Limitations of conventional treatments, such as adverse effects and antimicrobial resistance, have increased interest in plant-based alternatives. This article presents the phytochemical composition and pharmacological potential of several medicinal plants traditionally used in the treatment of skin diseases, including Rubus vulgaris, Plantago major, Artemisia terrae-albae, and Eryngium planum. Based on an analysis of scientific literature, the presence of bioactive compounds—including flavonoids, anthocyanins, phenolic acids, tannins, and sesquiterpenes—is summarized, along with their antioxidant, anti-inflammatory, and antimicrobial effects. Emphasis is placed on the correlation between traditional ethnomedicinal applications and pharmacological mechanisms. The findings support the potential of these species as sources for dermatological phytotherapeutics. Further research is needed to standardize active constituents, assess safety, and conduct clinical validation. Full article

Corn is one of the most consumed cereals in the Mexican diet. In this country, there are multiple varieties that exhibit nutraceutical potential due to their content of different metabolites with biological activity, such as blue corn. Another variety that has received little study is the red pigmented corn variety Chilac from Puebla, Mexico, which is being studied for its nutraceutical potential. A differential extraction using the Soxhlet method was carried out to evaluate the phenolic content, total flavonoid content, and monomeric anthocyanins, and free radical scavenging test was performed using the DPPH reagent. A proximate analysis was also conducted to identify the main macronutrients. The results of the proximate analysis were comparable to those of other traditional corn varieties, with carbohydrates being the macronutrient present in the highest amount at 77.9%. Regarding phenolic content and the presence of anthocyanins, the best extractions were obtained using alcoholic solvents; for example, ethanol for phenols, yielding 1368.420 ± 104.094 mg of gallic acid equivalents (GAE)/kg plant. In contrast, the flavonoid content was higher in the aqueous extract, with 833.984 ± 65.218 mg QE/Kg. In the case of the DPPH assay, the best result was obtained with ethyl acetate (73.81 ± 5.31%). These findings provide a foundation for expanding the use of corn varieties with nutraceutical potential, opening the possibility of studies focused on deeper characterization. Full article

The Amazon region is home to a remarkable diversity of plant species that are used in traditional medicine and cuisine. This study aimed to evaluate the functional, antioxidant, and antimicrobial properties of the leaves of Allium schoenoprasum, Brugmansia candida (white and pink), and Cyclanthemum bipartitum. Bioactive compounds (L-ascorbic acid, organic acids, carotenoids, phenolic compounds, and chlorophylls) were quantified using liquid chromatography. The ABTS and DPPH methods were used to assess the antioxidant capacity. Additionally, the antimicrobial activity against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Streptococcus mutans, Candida albicans, and Candida tropicalis was evaluated. The results revealed a high content of L-ascorbic acid (7.6 mg/100 g dry weight) and total carotenoids (509.0 mg/100 g dry weight), as well as high antioxidant capacity (4.5 mmol TE/100 g dry weight) and broad antimicrobial activity in Brugmansia candida ‘pink’. The White variety had the highest concentration of total chlorophylls (1742.8 mg/100 g DW), Cyclanthemum bipartitum had the highest total organic acid content (2814.5 mg/100 g DW), and Allium schoenoprasum had the highest concentration of total phenolic compounds (11,351.6 mg/100 g DW). These results constitute a starting point for future research, emphasizing the potential health risks that certain species may pose. Full article

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the equipment within the chiller plants of central air-conditioning systems. Traditional modeling approaches have been static and have focused on modeling within narrow time frames when a certain amount of equipment operating data has accumulated, thus prioritizing the precision of the model itself while overlooking the fact that energy-saving retrofits are a long-term process. This study proposes a modeling scheme for the equipment within chiller plants throughout the energy-saving retrofit process. Based on the differences in the amount of available operating data for the equipment and the progress of retrofit implementation, the retrofit process was divided into three stages, each employing different modeling techniques and ensuring smooth transitions between the stages. The equipment within the chiller plants is categorized into two types based on the clarity of their operating characteristics, and two modeling schemes are proposed accordingly. Based on the proposed modeling scheme, chillers and chilled-water pumps were selected to represent the two types of equipment. Real operating data from actual retrofit projects was used to model the equipment and evaluate the accuracy of the model predictions. The results indicate that the models established by the proposed modeling scheme exhibit good accuracy at each stage of the retrofit, with the coefficients of variation (CV) remaining below 6.88%. Furthermore, the prediction accuracy improved as the retrofitting process progressed. The modeling scheme performs better on equipment with simpler and clearer operating characteristics, with a CV as low as 0.67% during normal operation stages. This underscores the potential application of the proposed modeling scheme throughout the energy-saving retrofit process and provides a model foundation for the subsequent optimization of the refrigeration system. Full article

Thermal Power Plants (TPPs), as widely used industrial facilities for electricity generation, represent a key task in remote sensing image interpretation. However, detecting TPPs remains a challenging task due to their complex and irregular composition. Many traditional approaches focus on detecting compact, small-scale objects, while existing composite object detection methods are mostly part-based, limiting their ability to capture the structural and textural characteristics of composite targets like TPPs. Moreover, most of them rely on single-modality data, failing to fully exploit the rich information available in remote sensing imagery. To address these limitations, we propose a novel Cross-Modal Frequency-Responsive Attention Network (CFRANet). Specifically, the Modality-Aware Fusion Block (MAFB) facilitates the integration of multi-modal features, enhancing inter-modal interactions. Additionally, the Frequency-Responsive Attention (FRA) module leverages both spatial and localized dual-channel information and utilizes Fourier-based frequency decomposition to separately capture high- and low-frequency components, thereby improving the recognition of TPPs by learning both detailed textures and structural layouts. Experiments conducted on our newly proposed AIR-MTPP dataset demonstrate that CFRANet achieves state-of-the-art performance, with a ${\mathrm{mAP}}_{50}$ of 82.41%. Full article