Search Results (169)

Stevia (Stevia rebaudiana Bertoni) is an important source of natural sweeteners. Since its commercial value depends on steviol glycosides, quality assessment primarily involves quantifying these compounds in leaves and shoots. While chromatography is the standard analytical method, it is labor-intensive and time-consuming; it involves multiple processing steps that may cumulatively introduce errors and remains relatively expensive. Although chromatography remains the most accurate method, this exploratory study evaluates the potential of using spectroscopy as an auxiliary method for the approximate assessment of steviol glycoside content. Leaf reflectance spectroscopy could be a simpler and more cost-effective approach. However, relationships between leaf reflectance and steviol glycoside content are indirect and mediated by physiological processes. To account for these indirect dependencies, cumulative UVB exposure was included as an additional feature because it influences both leaf optical properties and plant metabolic processes. A low-cost spectrometer was utilized as the measuring instrument. The study was conducted over a period of three months on 77 S. rebaudiana clones, divided into four groups based on their level of UVB irradiance (control without irradiation, 400, 600, and 800 μW m⁻²). Based on the collected data, linear and polynomial regression, Random Forest, XGBoost, PLSR, and ElasticNetCV models were trained. Cumulative UVB exposure was found to be the most important feature. Of the spectral features, the most informative for assessing the content of steviol glycosides were spectral indicators in the far-red and near-infrared (NIR) ranges. Our results indicate a detectable relationship, with Random Forest being the best-performing model and achieving a moderate predictive performance (R² = 0.66). Despite their limited predictive performance, the models demonstrate that leaf reflectance spectra combined with cumulative UVB exposure contain information related to steviol glycoside content. These findings support further investigation of remote sensing approaches for crop quality assessment. Full article

To accurately quantify the intrinsic absorption efficiency of bamboo leaves to the solar spectrum, we measured the reflectance and transmittance of leaves from 55 bamboo species cultivated at the same site, and developed a mathematical model to calculate the annual cumulative photon absorption of photosynthetically active radiation (PAR) per leaf. The results showed the following: (1) Bamboo leaf optical properties exhibited high instrumental and spatial measurement consistency, with transmittance not significantly fluctuating with changes in incident light intensity or quality. (2) Bamboo leaves exhibited significant spectral selective absorption characteristics, with stronger absorption of blue and red light and weaker absorption of green light; Phyllostachys vivax had the highest mean absorptance per unit area, while Chimonobambusa tumidinoda had the lowest. (3) The annual photon absorption per unit leaf area ranged from 1.83 × 10⁵ to 9.86 × 10⁵ μmol, with Phyllostachys iridescens being the lowest and Chimonobambusa marmorea the highest. The annual photon absorption per single leaf ranged from 1.84 × 10⁶ to 5.13 × 10⁷ μmol, with Indocalamus decorus achieving the highest total absorption due to its largest leaf area (114.9 cm²), while Bambusa multiplex var. riviereorum was the lowest. (4) All tested bamboo species showed consistent seasonal dynamics in photon absorption, with the highest in summer and lowest in winter. Although unit-area absorptance reflects the intrinsic light interception efficiency, leaf morphology has a substantial influence (explaining 99.56% of the variance) in determining total light acquisition per leaf. Full article

This research addresses the increasing demand for low-cost GNSS solutions in natural resources management and geodesy by comparing a dual-frequency RTK receiver and a single-frequency autonomous receiver under identical conditions. The novelty lies in the simultaneous testing of u-blox ZED-F9P and u-blox MAX-M10S receivers connected to a common antenna, eliminating different signal reception effects. The study also evaluates the horizontal accuracy and area determination accuracy and the influence of seasonal foliage. Experiments were conducted on three polygons with varying vegetation canopies during leaf-on and leaf-off periods. The ZED-F9P receiver demonstrated high accuracy and stability when using RTK corrections. Under canopy conditions, the average horizontal errors were 0.17–0.18 m during leaf-on and improved by 58% to approximately 0.07 m during leaf-off season. The average area determination errors remained below 2%, confirming its suitability for precise mapping. In contrast, the MAX-M10S receiver showed substantial variability under vegetation. Its average horizontal errors reached 1.5–3.0 m during leaf-on season, with the maximum errors exceeding 5 m. Its seasonal improvement ranged from 41 to 54%, while its area errors reached up to 14.7%. The study confirms that while vegetation cover and seasonal foliage are limiting factors for both types of devices, low-cost RTK receivers represent a viable alternative to expensive professional instruments, even in more challenging conditions. Full article

This study investigated the aroma characteristics of three grades of raw tea leaves and their corresponding jasmine tea products from Guangxi, China. Aromatic profiles of jasmine tea varieties were analysed using two-dimensional gas chromatography-olfactory-mass spectrometry (GC×GC-O-MS), stir bar sorptive extraction (SBSE), and descriptive sensory evaluation. Chemometric methods were applied to compare sensory scores with instrumental data. Volatile compound concentrations and relative odour activity values (r-OAVs) were calculated. The results indicated significant differences in base tea leaf quality: high-grade tea leaf G1 exhibited pure, sweet characteristics, serving as an excellent aroma-absorbing carrier. The scenting process significantly imparted jasmine fragrance to the finished product, although its efficacy was constrained by tea leaf grade. GH1 finished tea exhibited a fresh, vibrant, and rich aroma with a sweet, mellow fragrance and high floral integration. In contrast, GH3, due to its inferior base material quality, yielded a weak aroma after scenting with limited quality improvement. The initial quality of the tea base is the fundamental determinant of the upper limit of the finished jasmine tea’s sensory quality, while the scenting process is the core means of shaping its signature floral aroma. The combination of high-quality tea leaves and precise scenting techniques is essential for developing the fresh, vibrant, and rich flavour profile of premium jasmine tea. This study reveals that the flavour formation of jasmine tea originates from the foundational quality of the tea leaves, providing a theoretical basis for monitoring the aroma quality of jasmine tea produced from different grades of tea leaves. Full article

Rapid, non-destructive estimation of leaf chlorophyll content (SPAD) is crucial for assessing plant photosynthetic health and nutrient status. However, conventional methods rely on specialized instruments (e.g., SPAD meters and hyperspectral sensors) which are costly, cumbersome, or unsuitable for large-scale field deployment. While RGB image analysis offers a low-cost alternative, most existing approaches depend solely on color features, which are susceptible to environmental interference and lack robustness across growth stages. To address these limitations, this study proposes a novel machine learning framework that fuses both color and texture features from smartphone-captured RGB images for accurate SPAD estimation in walnut seedlings and explores its linkage with potassium nutrition. ‘Wen 185’ walnut seedlings were subjected to seven potassium concentration treatments to induce a chlorophyll gradient. From the leaf images, 22 color indices and 8 texture features based on the Gray-Level Co-occurrence Matrix (GLCM) were extracted. Prediction models were built and compared using Random Forest (RF), XGBoost, and a Support Vector Machine (SVM), with two fusion strategies: data-level and feature-level fusion. Results demonstrated that the RF model with feature-level fusion achieved optimal performance (validation set: R² = 0.939, RMSE = 0.014, and RPD = 4.539), significantly outperforming models using single-feature types. SHAP analysis identified normalized red, normalized blue, and green-band correlation as the most influential features. This work fills a critical gap by establishing a robust, cost-effective, and interpretable method for SPAD monitoring using ubiquitous RGB imagery. Furthermore, the strong correlation between image-predicted SPAD and potassium levels confirms the method’s high potential for early and non-destructive diagnosis of potassium deficiency in orchard management. Full article

Erysiphe corylacearum, the causal agent of powdery mildew in hazelnut (Corylus avellana L.), has become an emerging pathogen of concern in Italian hazelnut production requiring rapid and accurate detection to support timely disease management and phytosanitary measures. We developed and validated a field-deployable loop-mediated isothermal amplification (LAMP) assay for the specific detection of E. corylacearum and evaluated three primer sets targeting the Internal Transcribed Spacer (ITS) region, RNA polymerase II second largest subunit (rpb2), and glutamine synthetase (GS) genes; the GS-targeting Ecg set showed the highest sensitivity and specificity. The assay was shown to be sensitive down to 200 fg of fungal DNA, efficiently detected E. corylacearum from diluted crude leaf extracts, and produced results within half an hour, allowing the detection of latent infections before visible symptoms emerged. On-site validation with a portable LAMP instrument showed the assay’s suitability for field-deployable diagnosis and early-warning applications in hazelnut orchards. Full article

Non-destructive estimation of high-temperature stress effects on tobacco plants is crucial for both scientific research and practical applications. Normalized difference vegetation index (NDVI), chlorophyll index, and spectra in the range of 900–1700 nm of Burley, Oriental, and Virginia tobacco plants under control and high-temperature stress conditions were measured using portable instruments. NDVI and chlorophyll index measurements indicate that young leaves of all tobacco types are tolerant to high temperatures. In contrast, the older leaves (the fifth leaf) showed increased sensitivity to heat stress. The chlorophyll content of these leaves decreased by 40 to 60% after five days of stress, and by the seventh day, the reduction reached 80% or more in all plants. The vegetative index of the fifth leaf also decreased on the seventh day of stress in all tobacco types. Differences in near-infrared spectra were observed between control, stressed, and recovered plants, as well as among different stress days, and among tobacco lines. The most significant differences were in the 1300–1500 nm range. The first characterization of heat-induced changes in the molecular structure of water in tobacco leaves using an aquaphotomics approach was conducted. Models for determining days of high-temperature treatment based on near-infrared spectra achieved a standard error of cross-validation (SECV) from 0.49 to 0.62 days. The total accuracy of the Soft Independent Modeling of Class Analogy (SIMCA) classification models of control, stressed, and recovered plants ranged from 91.0 to 93.6% using leaves’ spectra of the first five days of high-temperature stress, and from 90.7 to 97.7% using spectra of only the fifth leaf. Similar accuracy was obtained using Partial Least Squares–Discriminant Analysis (PLS-DA). Near-infrared spectroscopy and aquaphotomics can be used as a fast and non-destructive approach for early detection of stress and additional tools for investigating high-temperature tolerance in tobacco plants. Full article

The development of sustainable food systems requires natural solutions that reduce dependence on synthetic additives while ensuring instrumental quality, sensory acceptability, and consumer safety. This study evaluated Aristotelia chilensis leaf powder, a Chilean native plant rich in polyphenols, as a natural and sustainable alternative to synthetic antioxidants in the production of fresh sausages. The leaf powder was incorporated at 500, 1000, and 1500 mg/kg, and effects on proximal composition, pH, color, fatty acid profile, volatile organic compounds and sensory attributes were assessed. No changes were found in proximal composition or pH. Treatments with 500 and 1000 mg/kg increased oleic and eicosapentaenoic acids and reduced trans fatty acids and lower concentrations were observed of aldehydes linked to lipid oxidation. Overall, 500 mg/kg of maqui leaf powder (ML) was identified as the optimal treatment, as it reduced oxidation indicators, improved the lipid profile, and maintained sensory acceptability. In addition to its technological functionality and effect on sensory acceptability, the use of Aristotelia chilensis as a natural ingredient in sausages could reinforce food sustainability by reducing dependence on synthetic petroleum-derived additives, revaluing local biodiversity within a circular economy framework, and meeting consumer demand for healthier products with clean labels. Full article

Many industries release untreated synthetic dye effluents into water bodies, harming ecosystems and human health. Therefore, an economical and sustainable solution for treating dye-contaminated water must be developed. In this study, mulberry leaves (Morus nigra L.), as a cost-effective and sustainable adsorbent, were prepared to remove Basic Yellow 28 (BY28) and Basic Blue 3 (BB3) cationic dyes from industrial dye wastewater using adsorption. Batch experiments with key variables such as initial dye concentration, adsorbent dosage, contact time, temperature, stirring speed, and pH were conducted to find optimal conditions. The effectiveness of mulberry leaves as an adsorbent after multiple regeneration cycles was examined. The adsorbent was characterized through various instrumental methods, including FTIR, SEM, XRD, and BET analysis. Adsorption performance was analyzed using the Langmuir and Freundlich isotherm models. The results showed that the mulberry leaf adsorbent best fits the Langmuir model, with R² values of 0.999 for BY28 and 0.973 for BB3. The maximum adsorption capacities were 0.15 mg/g for BY28 and 7.19 mg/g for BB3, indicating their upper limits for dye uptake. The optimal conditions achieving removal efficiencies of over 99% were 1.5 g, 50 mL, 15 min, 180 rpm, and 10 mg/L at 30 °C for BY28 in neutral pH (7) and 1.5 g, 50 mL, 45 min, 100 rpm, and 30 mg/L at 40 °C for BB3 in basic pH (10). The regeneration of mulberry leaves as an adsorbent through acid treatment with 0.1 M HCl and 0.1 M CH₃COOH solutions maintained a high performance, achieving up to 98% dye removal efficiency after two regeneration cycles. It has been observed that successful results can be achieved in terms of reusability. Additionally, the removals of BB3 and BY28 performed in an ultrasonic-bath-assisted environment successfully achieved removal efficiencies of 84.87% and 75.41%, respectively. According to the results, mulberry leaves can effectively be used in wastewater treatment to remove dyes, can be reused multiple times, and thus serve as an environmentally friendly and sustainable adsorbent. Full article

In the design of operating procedures, structures, and control systems for agricultural machinery and equipment, it is necessary to fully consider data on the properties of relevant agricultural materials as the basis for research and design. Therefore, studying the physical properties of agricultural materials is of great significance. The basic physical parameters of agricultural materials include their shape, size, density, porosity, and moisture content. This study focuses on the triaxial dimensions, 1000-grain weight, moisture content, and tribological properties (sliding friction angle, natural repose angle) of the seeds of 16 varieties of small-seeded vegetables commonly grown in Hainan, including flowering Chinese cabbage, Chinese cabbage, lettuce, and leaf lettuce. Measurements were conducted using instruments such as a digital vernier caliper (Deli, Ningbo, China; accuracy 0.01 mm), an electronic balance (LICHEN, Shanghai, China; accuracy 0.001 g), a constant-temperature oven (Shangyi, Shanghai, China), and self-developed sliding friction angle and natural repose angle testers. Discrete element simulations were performed via EDEM 2021 software to validate the tribological properties by establishing particle models (spherical for flowering Chinese cabbage and Chinese cabbage; long–flat for lettuce and leaf lettuce) and instrument geometric models. Additionally, seed germinability (germination potential, germination rate, and germination speed) was tested using a constant-temperature incubation method. The results showed distinct differences between near-spherical and long–flat seeds in geometric characteristics, 1000-grain weight (2.27–3.06 g vs. 1.00–1.29 g), and tribological behavior (e.g., smaller natural repose angles for near-spherical seeds indicating better flowability). Plastic plates were identified as optimal for seed box guides due to lower sliding friction coefficients. EDEM 2021 simulations effectively verified the experimental data. High-germination-rate seeds (e.g., Hong Kong flowering Chinese cabbage, and Lifeng No.3 Chinese cabbage) were recommended for subsequent trials. These findings provide data support for the selection, design, and optimization of seed rope braiding machine components and sustainable seeding process. Full article

Leaf chlorophyll content is an important indicator of the health status of pear trees. This study used Korla fragrant pears, a Xinjiang regional product, to investigate methods for estimating the relative chlorophyll content of pear leaves. Samples were collected from pear trees in the east, south, west, and north positions of peripheral canopy leaves. The leaf soil plant analysis development (SPAD) method was implemented using a SPAD-502 laser chlorophyll meter. The instrument measures the relative chlorophyll content as the SPAD value. Leaf spectra were acquired using a portable field spectrometer, ASD FieldSpec4. ViewSpecPro 6.2 software was employed to smooth the ground spectral data. Traditional mathematical transformations and the discrete wavelet transform were used to process the spectral data, then correlation analysis was employed to extract the sensitive bands, and partial least squares regression (PLS) was used to establish a model for estimating the chlorophyll content of pear tree leaves. The findings indicate that (1) the models developed using the discrete wavelet transform had coefficients of determination (R²) exceeding 0.65, and their predictive performance surpassed that of other models employing various mathematical transformations, and (2) the model constructed using the L1 scale for the discrete wavelet transform had greater estimation accuracy and stability than models established through traditional mathematical transformations or the high-frequency scale for discrete wavelet transform, with an R² value of 0.742 and a root mean square error (RMSE) of 0.936. The prediction model for relative chlorophyll content established in this study was more accurate for chlorophyll monitoring in pear trees, and thus, it provided a new method for rapid estimation. Moreover, the model provides an important theoretical basis for the efficient management of pear trees. Full article

The assessment of air pollution is an important and relevant issue that requires continuous monitoring and control, especially in urban spaces. However, using instrumental air quality measurement techniques and deploying meters throughout the city is extremely expensive, so a biological alternative can be used—a bioindicator, i.e., a species whose vital functions or morphological structure can reveal the qualitative state of the environment. In this work, the lichen Hypogymnia physodes L. was used to analyze air pollution in areas of the provincial city of Opole, southern Poland. Microscope and chemotaxonomy methods were used in the laboratory to confirm field identification of lichens (atlases and keys). The selected elements, Mn, Fe, Ni, Cu, Zn, Cd, and Pb, were determined using atomic absorption spectrometry, and direct mercury analyzer was used to analyzed Hg concentration. Factor analysis (FA) was performed to associate elements with possible sources of air pollution. The highest concentrations of analytes were found at measurement points close to railway roads (Fe = 5131 mg/kg) and streets with heavy traffic (Pb = 101 mg/kg). Statistically significant differences (p < 0.001) were found between the concentrations of individual elements, which have positive correlation coefficients higher than 0.65. Based on the research carried out, different anthropogenic and traffic-related activities can be considered as one of the main sources of air pollution in Opole City based on the results of FA. Using an additional lichen scale, it can be concluded that the areas surveyed in the town of Opole can be classified as zone IV—characterized by an increase in the number of leaf lichens (additionally co-occurring lichens of the Polycauliona candelaria species), i.e., an area with an average level of air pollution (based also on contamination factor [CF] and pollution load index [PLI]). Accumulation concentrations of heavy metals in lichen were metal-specific and varied spatially, thus reflecting local differences in heavy metal deposition. The research presented here proves that low-cost passive biomonitoring can effectively support classical methods of assessing air pollution in urban spaces. Full article

Water is a crucial element for the growth of watermelon plants, making rapid and non-destructive monitoring of plant water content vital for precision irrigation in watermelon farming. While previous research has demonstrated the sensitivity of short-wave infrared (SWIR) bands to plant water content, their high costs limit widespread application. In contrast, visible and near-infrared (VNIR) spectral instruments offer significant advantages in terms of affordability, compactness, and spectral resolution. However, their potential for predicting the leaf water content (LWC) of watermelon plants has yet to be fully investigated. This study aims to assess the efficacy of hyperspectral reflectance measured with VNIR spectral instruments in estimating the LWC of watermelon plants at various leaf layers. Hyperspectral reflectance data (350−1100 nm) were collected from three leaf layers (upper, middle, and lower) under various drought treatments. Models for estimating LWC were developed using both spectral indices and full wavelength data. The results indicated that the middle leaf layer was the most effective for estimating LWC, and using full wavelength data achieved higher accuracy in LWC estimation. Furthermore, compared to the simple regression model, the AdaBoost-based machine learning model demonstrated superior performance, achieving an R² of 0.9636 in estimating LWC through five-fold cross-validation, which indicates high predictive accuracy. Ensemble learning significantly outperforms traditional methods, providing a substantial improvement in model accuracy. The findings offer important technical assistance for the spectral monitoring of LWC and precision irrigation in watermelon cultivation. Full article

The fraction of open Photosystem II (PSII) reaction centers (q_L) is critical for connecting broadband PSII fluorescence (ChlF_PSII) with the actual electron transport from PSII to Photosystem I. Accurately estimating q_L is fundamental for determining ChlF_PSII, which, in turn, is vital for mechanistically estimating the actual electron transport rate and photosynthetic CO₂ assimilation. Chlorophyll fluorescence provides direct physiological insights, offering a robust foundation for q_L estimation. However, uncertainties in the ChlF_PSII–q_L relationship across different plant functional types (PFTs) limit its broader application at large spatial scales. To address this issue, we developed a leaf-level instrument capable of simultaneously measuring actively and passively induced chlorophyll fluorescence. Using this system, we measured light response, CO₂ response, and temperature response curves across 52 species representing seven PFTs. Our findings reveal the following: (1) a strong linear correlation between ChlF_PSII derived from passively induced fluorescence and that from actively induced fluorescence (R² = 0.85), and (2) while the parameters of the ChlF_PSII–q_L relationship varied among PFTs, ChlF_PSII reliably modeled q_L within each PFT, with the R² ranging from 0.85 to 0.96. This study establishes quantitative ChlF_PSII–q_L relationships for various PFTs by utilizing passively induced fluorescence to calculate ChlF_PSII. The results demonstrate the potential for remotely sensed chlorophyll fluorescence data to estimate q_L and strengthen the use of fluorescence-based approaches for mechanistic GPP estimation at large spatial scales. Full article

As the effects of climate change progressively worsen, many scientists are concerned over the expanding geographic range and impact of forest-defoliating insects. Many are currently pointing to this form of disturbance becoming a key focus of remote sensing research in the coming decades; however, the available body of research remains lacking. This study investigated the viability of detecting and quantifying damage caused to a managed Scots pine forest in central Poland by insect defoliation disturbance using high-resolution multispectral satellite imagery. Observed leaf area index (LAI) values were compared to frass observations (insect detritus) to assess the relationship between LAI and defoliating insect activity across a single life cycle of A. posticalis Mats. Across four managed plots, four vegetative indices (NDVI, GNDVI, EVI, and MSAVI2) were calculated using multispectral satellite imagery from a PlanetScope (PSB.SD instrument) satellite system. Then, 1137 point-sampled digital number (DN) values were extracted from each index, and a correlation analysis compared each to 40 ground-observed LAI data points. LAI was modeled on the basis of NDVI values. Three models were assessed for their performance in predicting LAI. They were fit using a variety of regression techniques and assessed using several goodness-of-fit measures. A relationship between observed LAI and frass observations was found to be statistically significant (p-value = 0.000303). NDVI was found to be the correlated LAI values (rho = 0.612). Model 3, which was based on concepts of the Beer–Lambert law, resulted in the most robust predictions of LAI. All parameters were found to be significant post fitting of the model using a nonlinear least squares method. Despite the success of the Beer’s law model in predicting LAI, detection of A. posticalis damage was not achieved. This was predominately due to issues of resolution and plot condition, among others. The results of this analysis address many interesting facets of remote sensing analysis and challenge the commonly held view of the impeachability of these methods. Full article