Search Results (309)

The Soil and Plant Analyzer Development (SPAD) value is a reliable proxy for chlorophyll, yet conventional field measurement remains labor-intensive and spatially limited. Current remote sensing inversion models typically depend on costly multispectral sensors and rarely account for phenological changes, restricting their applicability across orchards and seasons. To overcome these limitations, this study introduces a stage-aware and low-cost SPAD inversion framework for jujube trees, integrating multi-source data fusion and an optimized ensemble model. A two-year experiment (2023–2024) combined UAV multispectral vegetation indices (VI) with RGB-derived color indices (CI) across leaf expansion, flowering, and fruit-setting stages. Rather than using static features, stage-specific predictors were systematically identified through a hybrid selection mechanism combining Random Forest Cumulative Feature Importance (RF-CFI), Recursive Feature Elimination (RFE), and F-tests. Building on these tailored features, XGBoost, decision tree (DT), CatBoost, and an Optimized Integrated Architecture (OIA) were developed, with all hyperparameters globally tuned using a genetic algorithm (GA). The RFI-CFI-OIA-GA model delivered superior accuracy (R² = 0.758–0.828; MSE = 0.214–2.593; MAPE = 0.01–0.045 in 2024) in the training dataset, and robust cross-year transferability (R² = 0.541–0.608; MSE = 0.698–5.139; MAPE = 0.015–0.058 in 2023). These results demonstrate that incorporating phenological perception into multi-source data fusion substantially reduces interference and enhances generalizability, providing a scalable and reusable strategy for precision orchard management and spatiotemporal SPAD mapping. Full article

Frozen vegetables are increasingly valued for their nutritional stability and year-round availability. This study provides a comprehensive assessment of twenty commercially available frozen vegetable blends obtained from retail markets in Poland. Analyses included physicochemical parameters, instrumental measurements of texture, color (CIEL*a*b*), and evaluation of technological quality. The pH values ranged from 4.40 to 7.46, total acidity from 0.034 to 0.322 g/100 g, and dry matter content from 5.02 to 42.97%. The observed variability was mainly attributable to vegetable type and remained consistent with values reported for fresh produce, indicating that industrial freezing largely preserves chemical characteristics. Texture differed markedly between vegetable types, with hardness values ranging from 6 to nearly 100 N, while color parameters remained within typical ranges for blanched and frozen vegetables, suggesting effective pigment stability and enzyme inactivation. In contrast, substantial variability was observed in technological quality. Mechanical fragmentation exceeded acceptable limits in 30% of samples, and complete clumping of vegetable pieces (100%) was observed. Additional defects included frostbite and color deviations, and health-condition defects were observed. These results highlight considerable heterogeneity in frozen vegetable blends and emphasize the need for stricter control of raw materials, processing conditions, and cold-chain management to ensure consistent quality and consumer safety. Full article

Earthwork volume calculation is a fundamental process in civil engineering and construction, requiring high-precision terrain data to assess ground stability encompassing load-bearing capacity, susceptibility to settlement, and slope stability and to ensure accurate cost estimation. However, seasonal and environmental constraints pose significant challenges to surveying. This study employed unmanned aerial vehicle (UAV) photogrammetry and light detection and ranging (LiDAR) mapping to evaluate the accuracy of digital terrain model (DTM) generation and earthwork volume estimation in densely vegetated areas. For ground extraction, color-based indices (excess green minus red (ExGR), visible atmospherically resistant index (VARI), green-red vegetation index (GRVI)), a geometry-based algorithm (Lasground (new)) and their combinations were compared and analyzed. The results indicated that combining a color index with Lasground (new) outperformed the use of single techniques in both photogrammetric and LiDAR-based surveying. Specifically, the ExGR–Lasground (new) combination produced the most accurate DTM and achieved the highest precision in earthwork volume estimation. The LiDAR-based results exhibited an error of only 0.3% compared with the reference value, while the photogrammetric results also showed only a slight deviation, suggesting their potential as a practical alternative even under dense summer vegetation. Therefore, although prioritizing LiDAR in practice is advisable, this study demonstrates that UAV photogrammetry can serve as an efficient supplementary tool when cost or operational constraints are present. Full article

The efficient detection of grape leaf nutrient parameters, including chlorophyll content, represented by soil and plant analysis development (SPAD), leaf nitrogen content (LNC), leaf potassium content (LKC), fresh weight water content (FWC), and dry weight water content (DWC), is crucial in precision agriculture. This study introduces a modeling framework that integrates hyperspectral preprocessing, feature selection, and multimodal data fusion. This framework enhances feature representation and model robustness by fusing spectral features (Ref), vegetation indices (VIS), and color and texture features from hyperspectral and red, green, and blue (RGB) images. Comparative experiments based on partial least squares regression (PLSR), Gaussian Process regression (GPR), and Bayesian Ridge regression (BRR) demonstrate that with a limited sample size, the PLSR and BRR models exhibit superior predictive performance and stability. However, during the optimization process, the performance improvement of the GPR model was the greatest (with R² increasing by up to 31.9%). Among the features, vegetation indices showed relatively high correlations with various traits. For image features, hyperspectral texture characteristics performed best, while color features from RGB images contributed significantly. Following preprocessing, feature selection, and feature combination, the performance of all models, except for DWC, improved progressively. Notably, feature selection significantly increased model accuracy. These findings indicate that multi-stage collaborative optimization strategies can be employed for the precise prediction of grape leaf functional traits. Full article

Older adults and patients with masticatory and deglutition disorders often experience difficulties consuming tough, fibrous vegetables. The enzymatic and liposomal conditions for softening garlic scapes were optimized while simultaneously enhancing their nutritional value through vitamin E fortification. Enzymes (Plantase UF and Plantase PT) were applied at varying concentrations and incubation times to determine optimal tenderization conditions, followed by the application of vitamin E-loaded liposomes. The physicochemical, microstructural, and color characteristics of the scapes and liposomal systems were evaluated. Enzymatic treatment significantly (p < 0.05) decreased hardness and increased adhesiveness, indicating effective cell wall disruption. Plantase PT hydrolyzes pectin in the middle lamella, promoting cell separation and softening, and maintains higher activity than Plantase UF, confirming its suitability for the consistent tenderization of fibrous vegetables. Its stability ensures reliable and uniform softening for real-world fibrous vegetable processing. Enzyme–vitamin E co-encapsulation balanced texture and nutrition by enlarging particles and lowering the ζ-potential (p < 0.05). Liposomal encapsulation preserved enzyme activity during processing and enabled sustained vitamin E delivery to scape tissues. Compared with untreated control, vitamin E liposomes provided controlled softening and improved nutrient stability. This highlights the potential of enzyme–liposome systems in developing tenderized older adult-friendly diets using fibrous plants. Full article

Natural dyes have emerged as a promising alternative to synthetic dyes for industrial applications due to their advantages, namely, easy availability, low cost, and environmental friendliness. In this sense, natural dyes, due to their potential to react over the pH range, could offer an alternative to conventional pH measuring techniques for industrial products, such as potentiometers, sensors, or indicator drops. Therefore, this project aims to evaluate the potential of several natural organic dyes in response to changes in pH and develop an indicator for determining pH grades. We extracted and analyzed the pigments of forty natural vegetable species using two extraction methods with a mixture of solvents, specifically 70% MeOH/30% H₂O. The results find that pigments of cabbage, hibiscus flower, radish, and turmeric in their dry state exhibit the best reaction over a broad pH range, and color can be easily distinguished according to its level. These findings demonstrate the potential of natural dyes as a novel approach for pH verification, providing a sustainable and cost-effective alternative to conventional techniques. Full article

The valorization of agricultural by-products such as beetroot stalks (BSs) offers a sustainable strategy for reducing food waste while enhancing nutritional value of staple foods. This study investigates the incorporation of BS powder, an agricultural waste rich in phenolics, betalains, and dietary fibers, into durum wheat semolina pasta. Pasta containing 5–20% BS were evaluated for bioactive compounds, cooking performance parameters, texture, color, and sensory acceptance. Enrichment increased total phenolics, antioxidant activity, and betalain concentration in a dose-dependent manner, with 20% BS pasta reaching 2.24 mg gallic acid equivalents/g phenolics and 1.53 mg/g betalains. Although drying and boiling reduced bioactive retention, enriched pasta maintained up to eightfold higher antioxidant activity than the control. Cooking performance showed increased water uptake and swelling index at higher substitution levels, while texture analysis revealed reduced hardness and cohesiveness above 15% BS substitution. Color analysis confirmed intense red hues from betalain pigments, enhancing consumer perception. Sensory evaluation indicated that control pasta was preferred for flavor and texture, but 10–15% BS samples were well accepted for their appealing color and mild vegetal notes. Overall, BS powder demonstrates strong potential for upcycling agricultural waste into functional, sustainable pasta with enhanced nutritional quality and alignment with circular economy practices. Full article

In the context of global climate change and rapid urbanization, the Urban Heat Island (UHI) effect has become a pressing environmental and public health concern, particularly in semiarid regions. This study evaluates the microclimatic performance of various urban design strategies aimed at enhancing thermal comfort along a densely built-up street in Van, a medium-sized city located in Turkey’s semiarid climate zone. Using ENVI-met 5.7.2, nine alternative scenarios were simulated, incorporating different configurations of vegetation cover (0%, 25%, 50%, 75%), ground surface materials, and green roof applications (0%, 25%, 50%, 75%). Physiological Equivalent Temperature (PET) and other thermal comfort indicators were assessed at multiple time intervals on the hottest summer day. Results indicate that increasing vegetation cover substantially reduces PET values, with a maximum reduction of 3.0 °C observed in the 75% vegetation scenario. While the scenario with no vegetation but light-colored pavements achieved a 1.8 °C reduction in air temperature at 2:00 p.m., the maximum PET value remained unchanged. Conversely, using dark-colored asphalt decreased the average air temperature by 1 °C and improved the thermal comfort level by reducing the PET by 0.4 °C compared to a non-vegetated scenario. The scenario with the highest overall greenery led to a 2.9 °C drop in air temperature and a 12.8 °C reduction in average PET at 2:00 p.m. compared to other scenarios. The study provides evidence-based recommendations for human-centered urban planning and advocates for the integration of microclimate simulation tools in the early stages of urban development. Full article

Using Shanghai as a case study, this paper develops a multi-source fusion and interpretable machine learning framework. Sentiment indices were extracted from Weibo check-ins with ERNIE 3.0, street-view elements were identified using Mask2Former, and urban indicators like the Normalized Difference Vegetation Index, floor area ratio, and road network density were integrated. The coupling between residents’ sentiments and streetscape features during heatwaves was analyzed with Extreme Gradient Boosting, SHapley Additive exPlanations, and GeoSHAPLEY. Results show that (1) the average sentiment index is 0.583, indicating a generally positive tendency, with sentiments clustered spatially, and negative patches in central areas, while positive sentiments are concentrated in waterfronts and green zones. (2) SHapley Additive exPlanations analysis identifies NDVI (0.024), visual entropy (0.022), FAR (0.021), road network density (0.020), and aquatic rate (0.020) as key factors. Partial dependence results show that NDVI enhances sentiment at low-to-medium ranges but declines at higher levels; aquatic rate improves sentiment at 0.08–0.10; openness above 0.32 improves sentiment; and both visual entropy and color complexity show a U-shaped relationship. (3) GeoSHAPLEY shows pronounced spatial heterogeneity: waterfronts and the southwestern corridor have positive effects from water–green resources; high FAR and paved surfaces in the urban area exert negative influences; and orderly interfaces in the vitality corridor generate positive impacts. Overall, moderate greenery, visible water, openness, medium-density road networks, and orderly visual patterns mitigate negative sentiments during heatwaves, while excessive density and hard surfaces intensify stress. Based on these findings, this study proposes strategies: reducing density and impervious surfaces in the urban area, enhancing greenery and quality in waterfront and peripheral areas, and optimizing urban–rural interfaces. These insights support heat-adaptive and sustainable street design and spatial governance. Full article

The growing global population has highlighted the need to replace animal-based meat with plant-based meat (PBM) as a protein source. Using lactic acid bacteria (LAB) offers a promising and sustainable approach to prolong PBM shelf life and maintain quality comparable to non-food additives. This study investigated the potential of LAB to improve the qualities of PBM products. Three LAB strains, Lactiplantibacillus plantarum (LM), Lactiplantibacillus pentosus (LS), and Pediococcus acidilactici (PA) were selected from vegetable sources, and their effects on PBM shelf life were monitored for 21 days at 4 °C. Results showed that PBM samples treated with both Lactiplantibacillus spp. maintained consistent color properties throughout the cold storage period. Textural analysis revealed that the control samples exhibited the lowest hardness, springiness, gumminess, and chewiness, while LS-treated samples showed the highest values. Both Lactiplantibacillus spp. treated samples had pH values at less than 5, with no statistically significant differences. Volatile organic compounds were not impacted by LAB. LM-treated PBM exhibited higher amino acid content compared to LS and non-LAB-treated samples. Our findings showed that L. plantarum improved the texture and prolonged the shelf life of PBM products at 4 °C for 21 days. Results indicated that L. plantarum could be used as an alternative sustainable green biological preservative agent, serving as a clean label product. Full article

Alkyd resins (ARs) represent a significant development in synthetic polymers, being among the oldest ones and playing a crucial role in numerous applications, especially within the coating sector. The trend is moving towards replacing non-renewable resources in the production of ARs with bio-based alternatives, with the goal of creating more sustainable binder materials as part of the transition to a bioeconomy. 2,5-Furandicarboxylic acid (FDCA) serves as a promising biomass-derived “building block” to replace non-renewable petroleum-derived aromatic diacids and anhydrides in AR synthesis. Various vegetable oils, including sunflower seed (SFO) and linseed oils (LSO), were utilized along with pentaerythritol (P) and glycerol (G) as polyols. FTIR and ¹H NMR spectroscopies were conducted for the verification of alkyd structures. The synthesized ARs were assessed for their physico-chemical properties, including acid value, hydroxyl value, color, density, and viscosity. The performance of the resulting alkyd coatings, which are crucial for their commercial applications, was examined. Key factors such as drying time, hardness, adhesion, wettability, chemical and corrosion resistance, and UV stability were analyzed. All synthesized FDCA-based alkyd coatings demonstrate outstanding adhesion, good thermal stability up to 220 °C, and barrier properties for steel with |Z|_0.02Hz ~10⁶–10⁷ Ohm cm⁻², which render them suitable for the processing requirements of indoor coating applications. The higher temperature at 50% mass loss (T₅₀) for SFO-P (397 °C) and LSO-P (413 °C) as compared to SFO-G (380 °C) and LSO-G (394 °C) indicated greater resistance to thermal breakdown when pentaerythritol was used as a polyol. Replacing glycerol with pentaerythritol in FDCA-based ARs resulted in a viscosity increase of 1.2–2.4 times and an enhancement in hardness from 2H to 3H. FDCA-based ARs exhibited decreased tack-free time, enhanced thermomechanical properties, and similar hardness as compared to phthalic anhydride-based ARs, underscoring the potential of FDCA as a sustainable alternative to phthalic anhydride in the formulation of ARs, integrating a greater proportion of renewable components for wood coating applications. Full article

The expected population rise will require a maximum exploitation of agricultural lands with a consequent increase in the demand for freshwater for irrigation uses. Future trends predict increasing periods of drought stress, which may impact on crop performance and limit the future production. Pepper is one of the most economically important crops and globally consumed vegetables. This crop is highly demanding in terms of water supply, and so far, developing tolerant cultivars is one of the main targets for breeding. The aim of this study is to accurately determine how pepper plants react to water stress at the vegetative stage in order to select genotypes that better cope with drought. We implemented the PhenoHort Plant Eye phenotyping platform to precisely assess changes in plant architecture and morpho-physiological parameters on 25 cultivated pepper genotypes (Capsicum annuum) under drought stress conditions. Three different irrigation supply levels were considered, including the control, intense, and severe water stress, by irrigating every 24, 72, and 96 h, respectively. Daily monitoring of 20 traits allowed ~190,000 multispectral and tridimensional data points through scans over 6 weeks of cultivation, thus shedding light on changes in plant architecture and vegetation indices’ values during stress. The dissection of genotype (G) and treatment (T) interactions revealed that digital biomass and plant height traits were strongly affected by the T factor (more than 50% of total variance), whereas color and multispectral parameters were under greater genotypic control, accounting for 58.27% and 64.97% of the total variance for HUE and NPCI, respectively. The comparison of each accession with respect to the control and the application of multivariate models allowed us to select four drought-tolerant lines (G1, G2, G22, and G25) able to reduce the effects of drought on the morphological parameters and architecture of the plant with positive effects on vegetative indices. This work represents the first attempt to dissect the response of pepper under drought stress at the vegetative stage using a high-throughput and non-invasive phenotyping system, offering new insights for selecting resilient genotypes. Full article

Leaf color, as a key ornamental and quality trait in leafy vegetable sweet potato, is controlled by the coordinated regulation of multiple pigment metabolic pathways. To dissect the mechanisms underlying leaf color variation, the integrated transcriptomic and metabolomic analyses were performed on three contrasting phenotypes: green (G), yellow (Y), and purple-red (R). The results showed that purplish-red leaves accumulated the highest levels of anthocyanins (16.36 mg·g⁻¹) and total chlorophyll (2.54 mg·g⁻¹), indicating that the synergistic accumulation of anthocyanins and chlorophyll contributes to their dark pigmentation. In contrast, yellow leaves contained the lowest carotenoid content yet displayed the highest carotenoid-to-chlorophyll ratio (6.44), suggesting that reduced chlorophyll levels coupled with a relatively higher carotenoid proportion underlie the yellow phenotype. Green leaves exhibited a more balanced pigment profile, with a total chlorophyll content of 1.94 mg·g⁻¹. Transcriptomic profiling revealed elevated expression of anthocyanin biosynthetic genes CHS, CHI, F3H, and chlorophyll metabolism-related genes CHLG and CAO in purplish-red leaves, whereas carotenoid biosynthesis genes LCY and CYP97A3 showed specific regulation in yellow leaves. Collectively, these findings demonstrate that leaf color formation in leafy vegetable sweet potato is determined by the relative accumulation of chlorophylls, carotenoids, and anthocyanins, together with differential regulation of their biosynthetic pathways. This work provides novel insights into the molecular basis of leaf color variation and offers a theoretical foundation for genetic improvement of leafy vegetable sweet potato. Full article

Tea Anthracnose (TA), caused by fungi of the genus Colletotrichum, is one of the major threats to global tea production. UAV remote sensing has been explored for non-destructive and high-efficiency monitoring of diseases in tea plantations. However, variations in illumination, background, and meteorological factors undermine the stability of cross-temporal data. Data processing and modeling complexity further limits model generalizability and practical application. This study introduced a cross-temporal, generalizable disease monitoring approach based on UAV multimodal data coupled with relative-difference standardization. In an experimental tea garden, we collected multispectral, thermal infrared, and RGB images and extracted four classes of features: spectral (Sp), thermal (Th), texture (Te), and color (Co). The Normalized Difference Vegetation Index (NDVI) was used to identify reference areas and standardize features, which significantly reduced the relative differences in cross-temporal features. Additionally, we developed a vegetation–soil relative temperature (VSRT) index, which exhibits higher temporal-phase consistency than the conventional normalized relative canopy temperature (NRCT). A multimodal optimal feature set was constructed through sensitivity analysis based on the four feature categories. For different modality combinations (single and fused), three machine learning algorithms, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP), were selected to evaluate disease classification performance due to their low computational burden and ease of deployment. Results indicate that the “Sp + Th” combination achieved the highest accuracy (95.51%), with KNN (95.51%) outperforming SVM (94.23%) and MLP (92.95%). Moreover, under the optimal feature combination and KNN algorithm, the model achieved high generalizability (86.41%) on independent temporal data. This study demonstrates that fusing spectral and thermal features with temporal standardization, combined with the simple and effective KNN algorithm, achieves accurate and robust tea anthracnose monitoring, providing a practical solution for efficient and generalizable disease management in tea plantations. Full article

Understanding the dispersal capacity of Triatoma infestans, the main vector of Chagas disease in South America, is vital for vector control and managing recolonization after insecticide use. This study compares the seasonal frequency of melanic and non-melanic T. infestans morphs in Northwestern Córdoba Province, Argentina, and examines their association with environmental variables, morphometric traits, nutritional status, and flight capacity. Insects were collected at the beginning and end of the warm season. Dorsal coloration, morphometric traits, nutritional status, flight-related indices, climatic variables, and vegetation cover were recorded. Chromatic morph frequencies were analyzed using chi-square tests. Biological predictors were identified through multi-model inference, and environmental associations explored with Canonical Correspondence Analysis. Melanic individuals decreased from early to late warm season, especially males. Wing loading correlated strongly with morphotype, being higher in non-melanic forms. Pronotum size were also a significant predictor. Nutritional status had no clear effect. Cattle pasture cover and rainfall influenced morph frequency, mainly in males. These results reveal a complex interaction between phenotypic and environmental factors shaping color morph variation, highlighting the importance of understanding these dynamics to optimize vector surveillance and control in areas prone to reinfestation. Full article