Search Results (56)

Understanding post-fire visible greenness change is important for assessing spatial heterogeneity in mountainous burned landscapes, but satellite observations often cannot capture local variation. This study developed a workflow using Unmanned Aerial Vehicle (UAV) Red–Green–Blue (RGB) imagery for RGB-interpreted burn severity classification and Green Leaf Index (GLI)-derived visible greenness change analysis three years after fire. The workflow integrated object-based Random Forest (RF) classification, bi-temporal GLI difference ($\Delta$GLI) detection, and terrain-stratified analysis under RGB-only conditions. Object-based multi-feature representation, including a 41-dimensional (41D) feature set of color, texture, and gradient metrics, supported local burn severity mapping, although performance gain over the 23-dimensional (23D) set was modest and not statistically significant. The burned area was dominated by high and moderate severity classes. GLI-derived analysis showed limited visible greenness increase (mean $\Delta$GLI = 0.0058), with slightly more than half of pixels being positive; high severity areas had higher $\Delta$GLI, while low severity areas showed limited or negative values. $\Delta$GLI also varied across terrain, being higher on steeper slopes, mid-to-upper elevations, and east-facing aspects. The workflow provides a practical local-scale approach for post-fire analysis using high-resolution UAV RGB imagery, with results interpreted as case-specific visible greenness patterns rather than comprehensive ecological recovery. Full article

The physiological, biochemical, and morphometric responses of two lettuce cultivars (Lactuca sativa L.), Gypsy and Pomegranate Lace, which differ in terms of leaf morphology and anthocyanin pigmentation, were examined under moderate light (290 µmol m⁻² s⁻¹) with the addition of blue light (BL, peak at 450 nm), UV-B (peak at 306 nm), and their combinations. Continuous low-intensity UV-B (30 mW m⁻²) was applied for 48 h—during the day with white (WL, Red: 51%, Green: 38%, Blue: 11%) or white + blue (WL + BL, Red: 30%, Green: 22%, Blue: 48%) light and at night alone—to assess the effects of sustained UVR8 activation in the absence of visible light. In the Pomegranate Lace cultivar, which has wrinkled leaves and localized anthocyanin pigmentation, the combination of WL + BL + UV-B enhanced the chlorophyll and carotenoid contents, photosynthetic rate, and stomatal conductance, whereas respiration did not change. These coordinated changes indicate efficient integration of cryptochrome and UVR8 signaling, which sustains photochemical efficiency and stimulates phenolic and carotenoid accumulation, reinforcing antioxidant capacity. In the Gypsy cultivar, which is characterized by smooth leaves and uniform pigmentation, UV-B + BL increased g_S along with the rates of respiration and photosynthesis and improved PSII efficiency. However, both cultivars showed a decrease in biomass and leaf area. Nevertheless, both cultivars exhibited increased antioxidant capacity, but in Gypsy, the addition of BL or UV-B affected the antioxidant capacity and PSII photochemical efficiency more effectively than in the Pomegranate Lace, likely due to deeper penetration in leaves and lower reflectance. Thus, long-term low-intensity UV-B radiation acts as a regulatory spectral cue that differentially modulates photosynthetic and antioxidant pathways. Its integration with blue light enables cultivar-specific optimization of photochemical resistance and metabolic resilience. Full article

The objective of the study was to develop and validate a ground-based method using a depth image sensor equipped with depth, visible red, green, blue (RGB), and near-infrared bands to measure the leaf area index (LAI) based on the relative illuminance of foliage only. The method was applied in a Itajii chinkapin (Castanopsis sieboldii (Makino) Hatus. ex T.Yamaz. & Mashiba )forest in Aichi Prefecture, Japan, and validated by comparing estimates with conventional methods (LAI-2200 and fisheye photography). To apply the 5-band sensor to actual forests, a methodology is proposed for matching the color camera and near-infrared camera in units of pixels, along with a method for widening the exposure range through multi-step camera exposure. Based on these advancements, the RGB color band, near-infrared band, and depth band are converted into several physical properties. Employing these properties, each pixel of the canopy image is classified into upper foliage, lower foliage, sky, and non-assimilated parts (stems and branches). Subsequently, the LAI is calculated using the gap-fraction method, which is based on the relative illuminance of the foliage. In comparison with existing indirect LAI estimations, this technique enabled the distinction between upper and lower canopy layers and the exclusion of non-assimilated parts. The findings indicate that the plant area index (PAI) ranged from 2.23 to 3.68 m² m⁻², representing an increase from 33% to 34% compared to the LAI calculated after excluding non-assimilating parts. The findings of this study underscore the necessity of distinguishing non-assimilated components in the estimation of LAI. The PAI estimates derived from the depth image sensor exhibited moderate to strong agreement with the LAI-2200, contingent upon canopy rings (R² = 0.48–0.98), thereby substantiating the reliability of the system’s performance. The developed approaches also permit the evaluation of the distributions of leaves and branches at various heights from the ground surface to the top of the canopy. The novel LAI measurement method developed in this study has the potential to provide precise, reliable foundational data to support research in ecology and hydrology related to complex tree structures. Full article

This study used Jilin-1 satellite data and unmanned aerial vehicle (UAV)-collected visible-thermal infrared imagery to calculate twelve spectral indices and evaluate their effectiveness in distinguishing post-fire forest areas and identifying human-altered land-cover changes in Jinyun Mountain, Chongqing. The research goals included mapping wildfire impacts with M-statistic separability, measuring land-cover distinguishability through Jeffries–Matusita (JM) distance analysis, classifying land-cover types using the random forest (RF) algorithm, and verifying classification accuracy. Cumulative human disturbances—such as land clearing, replanting, and road construction—significantly blocked the natural recovery of burn scars, and during long-term human-assisted recovery periods over one year, the Red Green Blue Index (RGBI), Green Leaf Index (GLI), and Excess Green Index (EXG) showed high classification accuracy for six land-cover types: road, bare soil, deadwood, bamboo, broadleaf, and grass. Key accuracy measures showed producer accuracy (PA) > 0.8, user accuracy (UA) > 0.8, overall accuracy (OA) > 90%, and a kappa coefficient > 0.85. Validation results confirmed that visible-spectrum indices are good at distinguishing photosynthetic vegetation, thermal bands help identify artificial surfaces, and combined thermal-visible indices solve spectral confusion in deadwood recognition. Spectral indices provide high-precision quantitative evidence for monitoring post-fire land-cover changes, especially under human intervention, thus offering important data support for time-based modeling of post-fire forest recovery and improvement of ecological restoration plans. Full article

Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R² values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R² values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R² values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. Full article

‘Green island’ symptoms in the form of vivid green, round spots visible on the senescent leaves of many plants and trees are mostly the results of pathogenic colonization by fungi, and the greenish tissue is often dead. Therefore, this study investigates whether green spots observed on senescent Norway maple (Acer platanoides L.) leaves were still alive and photosynthetically active. The appearance of ‘green islands’ on the leaves of young Norway maple trees was observed from the autumn of 2019 to 2022 in an urban forest (Bialystok, eastern Poland). However, in the late summer (September) of 2023 and 2024, mostly tar spots caused by the fungus Rhytisma spp. on maple leaves could be observed, with only a few leaves having ‘green island’ symptoms. The percentage of ‘green island’ areas on senescent leaves observed during the 4 years (2019–2022) was influenced by a year of sampling (p < 0.001). A non-destructive physiological analysis of chlorophyll, flavonoids, and nitrogen balance index (NBI) in leaves revealed that these parameters were significantly lower in ‘green islands’ than in the summer leaves, but higher than in the senescent yellow area of the autumn leaves. In the case of anthocyanins, their level was significantly higher in ‘green islands’ than in yellow areas, although, in the summer leaves, anthocyanins were undetectable. The amount of chlorophyll and most photosynthetic parameters were significantly (p < 0.05) reduced in the ‘green islands’ of the senescent leaves compared to the mature green leaves. However, these parameters were significantly higher in the ‘green islands’ than in senescent yellow leaves. Carotenoid content in the ‘green island’ and yellow areas of senescent leaves were at the same level, twice as higher than in summer leaves. Green mature leaves and the ‘green islands’ on senescent leaves had the same structure and anatomy. The main differences concerned the chloroplasts, which were smaller and had less grana and starch grains, but had more plastoglobuli in ‘green island’ cells. The cells building the mesophyll in the yellow area of the leaf deteriorated and their chloroplasts collapsed. Epiphytes were present on the adaxial epidermis surface in all types of samples. Full article

Decreases in chlorophyll control the degradation of green plants during leaf senescence and fruit ripening processes. Our previous daily monitoring of the natural senescence processes of Japanese maple (Acer palmatum) leaves demonstrated initial slow and later fast chlorophyll (Chl) decrease rates. In this study, Chl decrease processes were monitored by in situ visible and near-infrared spectroscopy during heating of maple leaves to 30–200 °C. The initial decreases with time in the 640–720 nm band area, due mainly to chlorophyll a after the water decrease, were fitted by first-order kinetics. The obtained rate constants k₁ from 200 to 60 °C showed a quasi-linear trend on an Arrhenius plot with an activation energy Ea of 38 kJ·mol⁻¹, while those from 60 to 30 °C had a different trend with an Ea of 91 kJ·mol⁻¹. Since the previous natural faster Chl decrease rates are on the extension of the higher-temperature trend, this process might occur without the protection of proteins in the photosynthetic system. On the other hand, the previous natural slower Chl decrease rates are on the extension of the lower-temperature trend, and might have protein protection. Full article

The color of the plant leaves is a major concern in many areas of agriculture. Pigmentation and its pattern provide the possibility to distinguish genotypes and a basis for annual crop management practices. For example, the nutrient and water status of plants is reflected in the chlorophyll content of leaves that are strongly linked to the lamina coloration. Pests and diseases (virus or bacterial infections) also cause symptoms on the foliage. These symptoms induced by biotic and abiotic stressors often have a specific pattern, which allows for their prediction based on remote sensing. In this report, an RGB (red, green and blue) image processing system is presented to determine leaf lamina color variability based on RGB-based color indices. LeafLaminaMap was developed in Scilab with the Image Processing and Computer Vision toolbox, and the code is available freely at GitHub. The software uses RGB images to visualize 29 color indices and the R, G and B values on the lamina, as well as to calculate the statistical parameters. In this case study, symptomatic (senescence, fungal infection, etc.) and healthy grapevine (Vitis vinifera L.) leaves were collected, digitalized and analyzed with the LeafLaminaMap software according to the mean, standard deviation, contrast, energy and entropy of each channel (R, G and B) and color index. As an output for each original image in the sample set, the program generates 32 images, where each pixel is constructed using index values calculated from the RGB values of the corresponding pixel in the original image. These generated images can subsequently be used to help the end-user identify locally occurring symptoms that may not be visible in the original RGB image. The statistical evaluation of the samples showed significant differences in the color pattern between the healthy and symptomatic samples. According to the F value of the ANOVA analysis, energy and entropy had the largest difference between the healthy and symptomatic samples. Linear discriminant analysis (LDA) and support vector machine (SVM) analysis provided a perfect recognition in calibration and confirmed that energy and entropy have the strongest discriminative power between the healthy and symptomatic samples. The case study showed that the LeafLaminaMap software is an effective environment for the leaf lamina color pattern analysis; moreover, the results underline that energy and entropy are valuable features and could be more effective than the mean and standard deviation of the color properties. Full article

Due to the vulnerability of pepper (Capsicum spp.) and the virulence of tomato spotted wilt virus (TSWV), seasonal shortages and surges of prices are a challenge and thus threaten household income. Traditional bioassays for detecting TSWV, such as observation for symptoms and reverse transcription-PCR, are time-consuming, labor-intensive, and sometimes lack precision, highlighting the need for a faster and more reliable approach to plant disease assessment. Here, two imaging techniques—Red–Green–Blue (RGB) and hyperspectral imaging (using NDVI and wavelength intensities)—were compared with a bioassay method to study the incidence and severity of TSWV in different pepper accessions. The bioassay results gave TSWV an incidence from 0 to 100% among the accessions, while severity ranged from 0 to 5.68% based on RGB analysis. The normalized difference vegetative index (NDVI) scored from 0.21 to 0.23 for healthy spots on the leaf but from 0.14 to 0.19 for disease spots, depending on the severity of the damage. The peak reflectance of the disease spots on the leaves was identified in the visible light spectrum (430–470 nm) when spectral bands were studied in the broad spectrum (400.93–1004.5 nm). For the selected wavelength in the visible light spectrum, a high reflectance intensity of 340 to 430 was identified for disease areas, but between 270 and 290 for healthy leaves. RGB and hyperspectral imaging techniques can be recommended for precise and accurate detection and quantification of TSWV infection. Full article

Nanoparticles derived from biological sources are currently garnering significant interest due to their diverse range of potential applications. The purpose of the study was to synthesize Al-doped nanoparticles of zinc oxide (ZnO) from leaf extracts of Cucumis maderaspatanus and assess their antioxidant and antimicrobial activity using some bacterial and fungal strains. These nanoparticles were analyzed using X-ray diffraction (XRD), ultraviolet–visible (UV-vis) spectroscopy, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) with energy dispersive X-ray analysis (EDAX), transmission electron microscopy (TEM), and thermogravimetric analysis/differential thermal analysis (TG-DTA). The average crystalline size was determined to be 25 nm, as evidenced by the XRD analysis. In the UV-vis spectrum, the absorption band was observed around 351 nm. It was discovered that the Al-ZnO nanoparticles had a bandgap of 3.25 eV using the Tauc relation. Furthermore, by FTIR measurement, the presence of the OH group, C=C bending of the alkene group, and C=O stretching was confirmed. The SEM analysis revealed that the nanoparticles were distributed uniformly throughout the sample. The EDAX spectrum clearly confirmed the presence of Zn, Al, and O elements in the Al-ZnO nanoparticles. The TEM results also indicated that the green synthesized Al-ZnO nanoparticles displayed hexagonal shapes with an average size of 25 nm. The doping of aluminum may enhance the thermal stability of the ZnO by altering the crystal structure or phase composition. The observed changes in TG, DTA, and DTG curves reflect the impact of aluminum doping on the structural and thermal properties of ZnO nanoparticles. The antibacterial activity of the Al-ZnO nanoparticles using the agar diffusion method showed that the maximum zone of inhibition has been noticed against organisms of Gram-positive S. aureus compared with Gram-negative E. coli. Moreover, antifungal activity using the agar cup method showed that the maximum zone of inhibition was observed on Aspergilus flavus, followed by Candida albicans. Al-doping nanoparticles increases the number of charge carriers, which can enhance the generation of reactive oxygen species (ROS) under UV light exposure. These ROS are known to possess strong antimicrobial properties. Al-doping can improve the crystallinity of ZnO, resulting in a larger surface area that facilitates more interaction with microbial cells. The structural and biological characteristics of Al-ZnO nanoparticles might be responsible for the enhanced antibacterial activity exhibited in the antibacterial studies. Al-ZnO nanoparticles with Cucumis maderaspatanus leaf extract produced via the green synthesis methods have remarkable antioxidant activity by scavenging free radicals against DPPH radicals, according to these results. Full article

Light quality can be stated to be the identity of an artificial light source, and although the response of light quality may vary depending on the crop, the effect is clearly visible and can produce various results depending on the combination of an artificial light source. This study investigated the spectral reflectance, photosynthetic performance, and chlorophyll fluorescence of mini green romaine lettuce based on light quality. Most parameters related to spectral reflectance showed the best results under blue light, and photosynthetic performance was more effective with mixed light than with single-colored light, among which blue + red (BR)-LED was the most suitable. Red light was ineffective, showing mostly low results in parameters of spectral reflectance and photosynthetic performance. In the case of chlorophyll fluorescence, the light quality influenced photomorphogenesis, resulting in increased leaf length and width with R- and quantum dot (QD)-LED, which expanded the leaf area and allowed for more external light to be captured (ABS/RC and TRo/RC). However, the ratio of electronized energy (ETo/RC) was low, and the amount of energy dissipated as heat (DIo/RC) was high. Consequently, the degree of electron acceptor reduction and overall photosynthetic performance (PI_ABS and PI_total) were lower compared to other light qualities. Additionally, the contrasting results of QD-LED and BR-LED were attributed to the form of red light and the presence or absence of far-red light when comparing spectra. Principal component analysis also clearly distinguished light qualities for photosynthesis and growth. Growth was increased by red (R)- and QD-LED, while photosynthetic performance was increased by BR- and blue (B)-LED. Full article

Quantifying the effect of maize tassel on canopy reflectance is essential for creating a tasseling progress monitoring index, aiding precision agriculture monitoring, and understanding vegetation canopy radiative transfer. Traditional field measurements often struggle to detect the subtle reflectance differences caused by tassels due to complex environmental factors and challenges in controlling variables. The three-dimensional (3D) radiative transfer model offers a reliable method to study this relationship by accurately simulating interactions between solar radiation and canopy structure. This study used the LESS (large-scale remote sensing data and image simulation framework) model to analyze the impact of maize tassels on visible and near-infrared reflectance in heterogeneous 3D scenes by modifying the structural and optical properties of canopy components. We also examined the anisotropic characteristics of tassel effects on canopy reflectance and explored the mechanisms behind these effects based on the quantified contributions of the optical properties of canopy components. The results showed that (1) the effect of tassels under different planting densities mainly manifests in the near-infrared band of the canopy spectrum, with a variation magnitude of ±0.04. In contrast, the impact of tassels on different leaf area index (LAI) shows a smaller response difference, with a magnitude of ±0.01. As tassels change from green to gray during growth, their effect on reducing canopy reflectance increases. (2) The effect of maize tassel on canopy reflectance varied with spectral bands and showed an obvious directional effect. In the red band at the same sun position, the difference in tassel effect caused by the observed zenith angle on canopy reflectance reaches 200%, while in the near-infrared band, the difference is as high as 400%. The hotspot effect of the canopy has a significant weakening effect on the shadow effect of the tassel. (3) The non-transmittance optical properties of maize tassels reduce canopy reflectance, while their high reflectance increases it. Thus, the dual effects of tassels create a game in canopy reflectance, with the final outcome mainly depending on the sensitivity of the canopy spectrum to transmittance. This study demonstrates the potential of using 3D radiative transfer models to quantify the effects of crop fine structure on canopy reflectance and provides some insights for optimizing crop structure and implementing precision agriculture management (such as selective breeding of crop optimal plant type). Full article

Leaf wilting is one of the most intuitive morphological manifestations of plants under drought stress, and it is useful in drought resistance screening. However, existing quantitative leaf-wilting measurement methods lack simplicity and high-throughput capacity under field conditions, and there is a gap in the systematic drought resistance assessments. The present study was conducted in 2020, 2021, and 2022 using 100 inbred maize lines. The maize lines were subjected to three different water stress treatments: normal irrigation, moderate drought, and severe drought. The findings led to the design of a simplified image acquisition and processing platform for measuring the visible green leaf area. A new measurement index and quantitative formula for wilting have been proposed, which effectively reflect leaf wilting and facilitate a systematic analysis of the relationship between yield and drought resistance. The results showed that the daily variation pattern of the visible green leaf area followed a trend of wilting first and then recovery, with maximum wilting occurring at noon (14:00–16:00 local time). The period of maximum wilting throughout the entire growth stage was the flowering stage. The maize plants with leaf wilt exceeding 1/2 (wilt ratio > 0.5) during the flowering stage were all low-yielding or low-tolerance inbred lines. In conclusion, this study emphasizes that the flowering stage is crucial for monitoring leaf wilting and introduces a quick high-throughput method to quantify leaf wilting. Our findings not only provide details about key indicators for identifying drought and heat resistance but also facilitate research on sustainable screening methods in maize, which will expedite the selection and accelerate the breeding of new varieties. Full article

The environmental changes in the Caatinga biome have already resulted in it reaching levels of approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, due to inadequate grazing practices that are driven by the difficulty of monitoring and estimating the yield parameters of forage plants, especially in agroforestry systems (AFS) in this biome. This study aimed to compare the predictive ability of different indexes with regard to the biomass and leaf area index of forage crops (bushveld signal grass and buffel grass) in AFS in the Caatinga biome and to evaluate the influence of removing system components on model performance. The normalized green red difference index (NGRDI) and the visible atmospherically resistant index (VARI) showed higher correlations (p < 0.05) with the variables. In addition, removing trees from the orthomosaics was the approach that most favored the correlation values. The models based on classification and regression trees (CARTs) showed lower RMSE values, presenting values of 3020.86, 1201.75, and 0.20 for FB, DB, and LAI, respectively, as well as higher CCC values (0.94). Using NGRDI and VARI, removing trees from the images, and using CART are recommended in estimating biomass and leaf area index in agroforestry systems in the Caatinga biome. Full article

The objective of this study was to verify the genetic dissimilarity and validate image phenotyping using RGB (red, green, and blue) sensors in tropical carrot germplasms. The experiment was conducted in the city of Carandaí-MG, Brazil, using 57 tropical carrot entries from Seminis and three commercial entries. The entries were evaluated agronomically and two flights with Remotely Piloted Aircraft (RPA) were conducted. Clustering was performed to validate the existence of genetic variability among the entries using an artificial neural network to produce a Kohonen’s self-organizing map. The genotype–ideotype distance index was used to verify the best entries. Genetic variability among the tropical carrot entries was evidenced by the formation of six groups. The Brightness Index (BI), Primary Colors Hue Index (HI), Overall Hue Index (HUE), Normalized Green Red Difference Index (NGRDI), Soil Color Index (SCI), and Visible Atmospherically Resistant Index (VARI), as well as the calculated areas of marketable, unmarketable, and total roots, were correlated with agronomic characters, including leaf blight severity and root yield. This indicates that tropical carrot materials can be indirectly evaluated via remote sensing. Ten entries were selected using the genotype–ideotype distance (2, 15, 16, 22, 34, 37, 39, 51, 52, and 53), confirming the superiority of the entries. Full article