Search Results (880)

Hydrangeas are economically important ornamental plants whose growth and flower characteristics depend on irrigation water quality (IWC), i.e., hydrogenionic potential (pH) and electrical conductivity (EC). Unfavorable IWC causes plant stress, reduced growth and tissue damage, leading to physiological responses such as proline accumulation (for stress protection) and increased malondialdehyde (MDA, an indicator of damage). A greenhouse pot experiment was conducted as a three-factor study in three replicates. The study examined the impact of different pH levels (4, 5, and 6, compared to a control treatment of pH 7), and EC levels (2, 3, and 4 dS m⁻¹, compared to a control treatment of 1 dS m⁻¹) on biomass, i.e., plant height (cm), stem number (n), flower number (n), leaf number (n) and weight (g), flower weight (g) and diameter (cm), growth index (GI) and the proline and MDA concentrations in two hydrangea varieties (Early Blue and Bianco). Study results showed a significant impact of (p = 0.0001) pH on all tested morphological properties, except flower diameter. Notably, pH 6 maximized biomass accumulation, i.e., plant height (56.6 cm), leaf number (n = 97) and weight, flower weight (156.8 g), and GI (36 cm), while pH 4 promoted the highest number of flowers (n = 10) and stems (n = 10), which are both crucial for aesthetic and market value. EC significantly (p = 0.001) affected plant height (EC 2 = 56.3 cm), flower (EC 2 = 181.9 g) and leaf weight (EC 3 = 148.2 g), and growth index (EC 2 = 27.2 cm). The lowest stress indicators (proline and MDA concentrations) were recorded at pH 6 (MDA = 0.215 µmol g⁻¹ FW, proline = 659.5 µmol g⁻¹ FW) and EC 2 (MDA = 0.551 µmol g⁻¹ FW, proline = 4068.2 µmol g⁻¹ FW). The highest MDA and proline concentrations were observed under extreme conditions of pH 4 (MDA = 1.257 µmol g⁻¹ FW, proline = 12,811.7 µmol g⁻¹ FW) and EC 4 (MDA = 0.692 µmol g⁻¹ FW, proline = 4880.8 µmol g⁻¹ FW). No significant effect of variety on proline and MDA concentrations was observed, while the highest GI was recorded for the Early Blue variety (24.3 cm). This research identifies pH 6 and EC 2 as key conditions for optimizing hydrangea biomass and reducing stress, offering practical guidelines for growers. The findings provide a foundation for developing precise irrigation water quality strategies in the commercial production of ornamental plants. Full article

Glucosinolates are secondary metabolites of the Brassicales, playing a role in plant protection and as health-promoting compounds. Here, Na₂SO₄ was used to modulate the aliphatic glucosinolate content in different organs of Eruca sativa Mill. In flowers, which accumulate the highest amount of glucosinolates, Na₂SO₄ increased the concentration of glucoraphanin, in roots of glucoerucin and in apical leaves it doubled the amount of dimeric 4-mercaptobutyl glucosinolate. The biosynthetic gene Branched-Chain Aminotransferase 4 was also induced in roots at the highest salt concentration, while in leaves all tested genes biosynthetic genes were downregulated or unaffected. Cytochromes P450 83A1 monooxygenase was downregulated at the highest salt concentration in all organs. Overall, E. sativa is a reliable source of glucosinolates, which can be modulated with Na₂SO₄. Full article

The structure of a scientific article is crucial for clearly conveying research findings. Modern scientific publications combine text with various elements—such as tables, graphs, images, diagrams, and maps—that support the narrative and aid data interpretation. Understanding how these components influence a publication’s reception and scientific impact is essential. This study analyzes differences among 15 soil science journals (indexed in the Web of Science) in terms of visual elements, tables, number of authors, and article length. The journals had a 5-year Impact Factor (2023) ranging from 0.9 (Soil and Environment) to 10.4 (Soil Biology and Biochemistry). The Kruskal–Wallis test and Bonferroni-adjusted Dunn’s post hoc tests revealed statistically significant differences across all variables (p < 0.05). The relationships were further assessed using Pearson’s correlation, based on the median number of authors and article length, as well as the percentage of articles that include at least one element of a given type (e.g., table, graph, image, diagram, or map). Key findings show that journals with a higher impact factor tend to publish articles with more authors (r = 0.62, p = 0.014), use diagrams more frequently (r = 0.69, p = 0.004), and include fewer tables (r = –0.85, p < 0.001). These results suggest that journals with a higher 5-year IF tend to include articles with a greater number of authors and a higher frequency of diagram use, while relying less on tables. Full article

Soil testing has long been used to optimize fertilization and crop production. More recently, soil health testing has emerged to reflect the growing interest in soil multifunctionality and ecosystem services. Soil health encompasses physical, chemical, and biological properties that support ecosystem functions and sustainable agriculture. Despite its relevance to several United Nations Sustainable Development Goals (SDGs 1, 2, 3, 6, 12, 13, and 15), comprehensive soil health testing is not widely practiced due to complexity and cost. The aim of the study presented here was to contribute to the further development, implementation, and testing of an integrated procedure for soil health assessment in practice. We developed and tested a rapid, standardized soil health assessment tool that combines near-infrared spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl₂ extraction with Inductive Coupled Plasma Mass Spectroscopy analysis. The tool evaluates a wide range of soil characteristics with high accuracy (R² ≥ 0.88 for most parameters) and has been evaluated across more than 15 countries, including those in Europe, China, New Zealand, and Vietnam. The results are compiled into a soil health indicator report with tailored management advice and a five-level ABCDE score. In a Dutch test set, 6% of soils scored A (optimal), while 2% scored E (degraded). This scalable tool supports land users, agrifood industries, and policymakers in advancing sustainable soil management and evidence-based environmental policy. Full article

We evaluated the inclusion of hydrolyzed feather meal (HFM) as a partial replacement for fishmeal in diets for Macrobrachium rosenbergii post-larvae (PL) over a 32-day nursery feeding trial. Five experimental diets with increasing HFM levels (control, 1.5%, 3.0%, 4.5%, and 6.0%) were tested. Survival rates ranged from 73.3 ± 5.44% to 83.3 ± 3.84% without significant differences among groups. Dietary HFM inclusion levels above 3.0% significantly improved prawn performance, including final weight (up to 2.18-fold higher than control), length (1.13-fold), antenna length (1.18-fold), biomass gain (2.14-fold), and feed conversion ratio (1.59-fold lower). Prawn-fed diets at 6.0% HFM showed the highest performance among all experimental groups. No significant effects were observed on antioxidant biomarkers or digestive enzymes in prawns hepatopancreas, which suggests no imbalance in the antioxidant system or impairment of digestive function. Likewise, carcass proximate composition remained stable across experimental groups. These findings suggest that HFM at 3.0–6.0% dietary inclusion levels is a potential alternative to fishmeal in nursery-phase diets for M. rosernbergii PL, promoting prawn growth and welfare and maintaining health and carcass quality. Notably, to the best of our knowledge, this is the first study demonstrating the potential effective use of HFM in feeding the nursery phase of M. rosernbergii. Full article

Aiming to address the problems of asynchronous acquisition time of multiple sensors in the crop phenotype acquisition system and high cost of the acquisition equipment, this paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol, realizing the synchronous acquisition of three types of crop data: visible light images, thermal infrared images, and laser point clouds. The paper innovatively proposed the Difference Structural Similarity Index Measure (DSSIM) index, combined with statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and Hausdorff distance to characterize the stability of the system. After 72 consecutive hours of synchronization testing on the timing boards, it was verified that the root mean square error of the synchronization time for each timing board reached the ns level. The synchronous trigger acquisition time for crop parameters under time synchronization was controlled at the microsecond level. Using pepper as the crop sample, 133 consecutive acquisitions were conducted. The acquisition success rate for the three phenotypic data types of pepper samples was 100%, with a DSSIM of approximately 0.96. The average point number difference and average coordinate error were both about 3%, while the Charm distance and Hausdorff distance were only 1.14 mm and 5 mm. This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction. Full article

Yellow rust (Puccinia striiformis) is a common wheat disease that significantly reduces yields, particularly in seasons with cooler temperatures and frequent rainfall. Early detection is essential for effective control, especially in key wheat-producing regions such as Southern Dobrudja, Bulgaria. This study presents a UAV-based approach for detecting yellow rust using only RGB imagery and deep learning for pixel-based classification. The methodology involves data acquisition, preprocessing through histogram equalization, model training, and evaluation. Among the tested models, a UnetClassifier with ResNet34 backbone achieved the highest accuracy and reliability, enabling clear differentiation between healthy and infected wheat zones. Field experiments confirmed the approach’s potential for identifying infection patterns suitable for precision fungicide application. The model also showed signs of detecting early-stage infections, although further validation is needed due to limited ground-truth data. The proposed solution offers a low-cost, accessible tool for small and medium-sized farms, reducing pesticide use while improving disease monitoring. Future work will aim to refine detection accuracy in low-infection areas and extend the model’s application to other cereal diseases. Full article

The environmental impact of milk production, particularly its share of greenhouse gas (GHG) emissions, is a topic under investigation in various parts of the world. This paper presents an overview of current knowledge on the carbon footprint (CF) of milk production at the farm level, with a particular focus on technological, environmental and organisational factors affecting emission levels. The analysis is based on a review of, inter alia, 46 peer-reviewed publications and 11 environmental reports, legal acts and databases concerning the CF in different regions and under various production systems. This study identifies the main sources of emissions, including enteric fermentation, manure management, and the production and use of feed and fertiliser. It also demonstrates the significant variability of the CF values, which range, on average, from 0.78 to 3.20 kg CO₂ eq kg⁻¹ of milk, determined by the farm scale, nutritional strategies, local environmental and economic determinants, and the methodology applied. Moreover, this study stresses that higher production efficiency and integrated farm management could reduce the CF per milk unit, with further intensification having, however, diminishing effects. The application of life cycle assessment (LCA) methods is essential for a reliable assessment and comparison of the CF between systems. Ultimately, an effective CF reduction requires a comprehensive approach that combines improved nutritional practices, efficient use of resources, and implementation of technological innovations adjusted to regional and farm-specific determinants. The solutions presented in this paper may serve as guidelines for practitioners and decision-makers with regard to reducing GHG emissions. Full article

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. Full article

The present study highlights the critical role of surface type, insect species, and exposure duration in determining the efficacy of surface-applied insecticides in stored-product pest management. Four insecticides were sprayed and evaluated on different surfaces (concrete, metallic, plastic, and ceramic) against two beetles: the red flour beetle and the tobacco beetle. Alpha-cypermethrin and spinosad exhibited rapid and high efficacy, particularly on non-porous surfaces such as metal and ceramic, whereas pirimiphos-methyl was less effective initially and required extended exposure to achieve complete mortality, especially against Tribolium castaneum. In contrast, Lasioderma serricorne showed greater susceptibility across all insecticides and surfaces. Spinosad maintained high efficacy across all surface types, suggesting broader applicability under variable conditions. The reduced performance of insecticides on concrete surfaces underscores the influence of substrate porosity on insecticide bioavailability. Additionally, the observed delayed mortality effect in all treatments indicates that even brief exposure can result in lethal outcomes, emphasizing the long-term potential of these applications. These findings underscore the need for surface-specific application strategies and support the integration of surface treatments into comprehensive pest management programs. Further research is warranted under simulated field conditions to assess residual efficacy over time and in the presence of food, thereby enhancing the relevance of laboratory findings to real-world storage environments. Full article

Shrimp farming is practiced worldwide in tropical and subtropical regions, where shrimp often experience suboptimal temperatures during part of the production cycle, resulting in slower growth. A concentrated functional palatability enhancer (FPE) containing a mixture of chemoattractants was tested. A 12-week experiment at a suboptimal temperature (22 °C) was conducted with Penaeus vannamei (3.25 ± 0.02 g) in a clear water system (400 L with 40 shrimp per tank) with flow-through seawater. A standard diet was supplemented with 0, 1, and 2 g kg⁻¹ of FPE (STD, STD+1, and STD+2) with four replicates for each one. The inclusion of 1 g kg⁻¹ of FPE (STD+1) significantly increased the average final weight by 11.24% and weekly weight gain by 14,00% when compared to STD. The highest tested dose (2 g kg⁻¹) did not result in further improvement in growth performance compared to the control. In addition, the total hemocyte count (THC) remained at an optimal level for the species in the STD+1 treatment under suboptimal temperature conditions compared to the other treatments. We also observed a decrease in Vibrio spp. bacterial counts in STD+1 compared to STD+2. Therefore, the lowest tested dose was shown to positively influence the rearing of P. vannamei at suboptimal temperatures. Full article

Seedling transplantation, a pivotal component in advancing the cultivation of vegetables and cash crops, significantly bolsters crops’ resilience against drought, cold, pests, and diseases, while substantially enhancing yields. The implementation of transplanting machinery not only remarkably alleviates the labor-intensive nature of transplantation but also elevates the precision and uniformity of the process, thereby facilitating mechanized plant protection and harvesting operations. This article summarizes the research status and development trends of mechanized field transplanting technology and equipment. It also analyzes and summarizes the types and current status of typical representative automatic seedling picking mechanisms. Based on the current research status, the challenges of mechanized transplanting technology were analyzed, mainly the following: insufficient integration of agricultural machinery and agronomy; the standards for each stage of transplanting are not perfect; the adaptability of existing transplanting machines is poor; the level of informatization and intelligence of equipment is low; the lack of innovation in key components, such as seedling picking and transplanting mechanisms; and the proposed solutions to address the issues. Corresponding solutions are proposed, such as the following: strengthening interdisciplinary collaboration; establishing standards for transplanting processes; enhancing transplanter adaptability; accelerating intelligentization and digitalization of transplanters; strengthening the theoretical framework; and performance optimization of transplanting mechanisms. Finally, the development direction of future fully automatic transplanting machines was discussed, including the following: improving the transplanting efficiency and quality of transplanting machines; integrating research and development of testing, planting, and seedling supplementation for transplanting machines; unmanned transplanting operations; and fostering collaborative industrial development. Full article

The accurate prediction of soil organic matter (SOM) content is essential for promoting sustainable soil management and addressing global climate change. Due to multiple factors such as topography and climate, especially in mountainous areas, SOM spatial prediction faces significant challenges. The main novelty of this study lies in proposing a geographic positional encoding mechanism that embeds geographic location information into the feature representation of a Transformer model. The encoder structure is further modified to enhance spatial awareness, resulting in the development of the Geo-Positional Transformer (GPTransformer). Furthermore, this model is integrated with a 1D-CNN to form a dual-branch neural network called the Geo-Positional Transformer-CNN (GPTransCNN). This study collected 1490 topsoil samples (0–20 cm) from cultivated land in Longshan County to develop a predictive model for mapping the spatial distribution of SOM across the entire cultivated area. Different models were comprehensively evaluated through ten-fold cross-validation, ablation experiments, and uncertainty analysis. The results show that GPTransCNN has the best performance, with an R² improvement of approximately 43% over the Transformer, 19% over the GPTransformer, and 15% over the 1D-CNN. This study demonstrates that by incorporating geographic positional information, GPTransCNN effectively combines the global modeling capabilities of the GPTransformer with the local feature extraction strengths of the 1D-CNN, which can improve the accuracy of SOM mapping in mountainous areas. This approach provides data support for sustainable soil management and decision-making in response to global climate change. Full article

Accurate, energy-efficient climate regulation is crucial for scaling smart greenhouse production. While nonlinear model predictive control (NMPC) can co-optimize yield and resource use, its efficacy hinges on short-range weather information and real-time solver feasibility. This paper investigates the performance of advanced NMPC strategies for smart greenhouse climate control, with particular emphasis on the roles of AI-driven disturbance prediction and warm-start initialization for real-time optimization. Six controller configurations, including feedback-only, LSTM-based forecast, and ideal disturbance models, each with and without warm-start, were tested in a 40-day simulation of a lettuce smart greenhouse. Performance metrics included final biomass, constraint violations, resource costs, profit, and solver time. Results show that feedback-only controllers maximize yield and profit, incurring higher CO₂ costs but lower heating costs, alongside greater constraint violations compared to the predictive strategies. Predictive and ideal disturbance-aware controllers effectively reduce resource consumption and improve constraint compliance at the expense of lower yields. Importantly, warm-start initialization significantly accelerates computation without affecting control quality. The study also demonstrates that penalty parameters, rather than economic weight settings, predominantly determine aggregate constraint violation. The findings provide actionable insights for designing and deploying NMPC-based greenhouse controllers, highlighting the importance of warm-start techniques and the trade-offs between productivity, resource efficiency, and environmental compliance. Full article

Humulus lupulus, commonly known as hop, is a climbing plant whose female cones impart beer’s characteristic bitterness and aroma and also serve as a preservative. In this study, we conducted a meta-analysis to investigate the antimicrobial activity of hop compounds and extracts against various microorganisms by statistically synthesizing minimum inhibitory concentration (MIC) values. From the 2553 articles retrieved from the comprehensive literature search, 18 provided data on MIC values for six hop compounds, and three extract types tested against 55 microbial strains’ MIC values corresponded to 24 and 48 h incubation periods with compounds or extracts. The results indicate that xanthohumol (a flavonoid) and lupulone (a bitter acid) exhibit potent antimicrobial activity against most tested microorganisms, particularly food spoilage bacteria [21.92 (95%CI 9.02–34.83), and 12.40 (95%CI 2.66–22.14) μg/mL, respectively, for 24 h of treatment]. Furthermore, hydroalcoholic extracts demonstrated greater efficacy compared to supercritical CO₂ (SFE) extracts, which showed limited antimicrobial effects against both probiotic and non-probiotic strains. These findings underscore the need for standardized, evidence-based protocols—including uniform microbial panels and consistent experimental procedures—to reliably evaluate the antimicrobial properties of hop-derived compounds and extracts. Taken together, our findings ultimately chart a path toward evidence based antimicrobial tests that could inform food-preservation strategies and inspire the development of plant-based antimicrobials. Full article