Search Results (513)

Monitoring crop damage caused by wildlife remains a significant challenge in agricultural management, particularly in the case of large-scale monocultures such as maize. The given study presents a semi-automated process for detecting wildlife-induced damage using RGB imagery acquired from unmanned aerial vehicles (UAVs). The method is designed for non-specialist users and is fully integrated within the QGIS platform. The proposed approach involves calculating three vegetation indices—Excess Green (ExG), Green Leaf Index (GLI), and Modified Green-Red Vegetation Index (MGRVI)—based on a standardized orthomosaic generated from RGB images collected via UAV. Subsequently, an unsupervised k-means clustering algorithm was applied to divide the field into five vegetation vigor classes. Within each class, 25% of the pixels with the lowest average index values were preliminarily classified as damaged. A dedicated QGIS plugin enables drone data analysts (Drone Data Analysts—DDAs) to adjust index thresholds, based on visual interpretation, interactively. The method was validated on a 50-hectare maize field, where 7 hectares of damage (15% of the area) were identified. The results indicate a high level of agreement between the automated and manual classifications, with an overall accuracy of 81%. The highest concentration of damage occurred in the “moderate” and “low” vigor zones. Final products included vigor classification maps, binary damage masks, and summary reports in HTML and DOCX formats with visualizations and statistical data. The results confirm the effectiveness and scalability of the proposed RGB-based procedure for crop damage assessment. The method offers a repeatable, cost-effective, and field-operable alternative to multispectral or AI-based approaches, making it suitable for integration with precision agriculture practices and wildlife population management. Full article

The context of global climate change, water stress has a significant impact on the ecological function and landscape value of urban greening shrubs. In this study, three typical greening shrubs (Euonymus japonicus, Ligustrum × vicaryi, and Berberis thunbergii var. atropurpurea) in North China were subjected to a two-year field-controlled experiment (2022–2023) with four water treatments: full irrigation, deficit irrigation, natural rainfall, and extreme drought. The key findings are as follows: (1) Extreme drought reduced the color indices substantially—the G_CC of E. japonicus decreased by 40% (2023); the R_CC of B. thunbergii var. atropurpurea declined by 35% (2022); and the color indices of L. × vicaryi remained stable (variation < 15%). (2) Early-season soil water content (SWC) strongly correlated with the color index of E. japonicus (r² = 0.42, p < 0.05) but weakly with B. thunbergii (r² = 0.28), suggesting species-specific drought-tolerance mechanisms like reduced leaf area. (3) Deficit irrigation (SWC ≈ 40%) maintained color indices between fully irrigated and drought-stressed levels. Notably, B. thunbergii retained high redness (R_CC > 0.8) at an SWC ≈ 40%; E. japonicus required an SWC > 60% to preserve greenness (G_CC). The research results provide a scientific basis for urban greening plant screening and water-saving irrigation strategies, and expand the application scenarios of color coordinates in plant physiological and ecological research. Full article

Curcuma spp. is a popular ornamental crop valued for its vibrant appearance and suitability for both regular and off-season production. As global emphasis on freshwater conservation increases and with a demand for compact potted plants, reducing water use while maintaining high aesthetic quality presents a key challenge for horticulturists. Potassium silicate (PS) has been proposed as a foliar spray to alleviate plant water stress. This study aimed to evaluate the effects of PS on growth, ornamental traits, and photosynthetic parameters of off-season potted Curcuma cv. ‘Jasmine Pink’ under deficit irrigation (DI). Plants were subjected to three treatments in a completely randomized design: 100% crop evapotranspiration (ETc), 50% ETc, and 50% ETc with 1000 ppm PS (weekly sprayed on leaves for 11 weeks). Both DI treatments (50% ETc and 50% ETc + PS) reduced plant height by 7.39% and 9.17%, leaf number by 16.99% and 7.03%, and total biomass by 21.13% and 20.58%, respectively, compared to 100% ETc. Notably, under DI, PS-treated plants maintained several parameters equivalent to the 100% ETc treatment, including flower bud emergence, blooming period, green bract number, effective quantum yield of PSII (ΔF/Fm′), and electron transport rate (ETR). In addition, PS application increased leaf area by 8.11% and compactness index by 9.80% relative to untreated plants. Photosynthetic rate, ΔF/Fm′, and ETR increased by 31.52%, 13.63%, and 9.93%, while non-photochemical quenching decreased by 16.51% under water-limited conditions. These findings demonstrate that integrating deficit irrigation with PS foliar application can enhance water use efficiency and maintain ornamental quality in off-season potted Curcuma, promoting sustainable water management in horticulture. Full article

Creeping bentgrass (Agrostis stolonifera L.) is an important cool-season turfgrass species widely used for golf course putting greens; however, it experiences a summer stress-induced quality decline in the U.S. transition zone and other regions with similar climates. The objective of this study was to determine the effects of five amino acid biostimulants on creeping bentgrass drought and heat stress tolerance. The five biostimulants, including Superbia, Amino Pro V, Siapton, Benvireo, and Surety, at the rate of 0.22 g of N m⁻², were applied biweekly to foliage, and the treatments were arranged in a randomized block design with four replications and were subjected to 56 days of heat and drought stress in growth chamber conditions. The amino acid biostimulants Superbia and Amino Pro V improved the turf quality, photochemical efficiency (PE), normalized difference vegetation index (NDVI), chlorophyll content, antioxidant enzyme superoxide dismutase activity, root growth, and viability and suppressed leaf H₂O₂ levels when compared to a control. Among the treatments, Superbia and Amino Pro V exhibited greater beneficial effects on turf quality and physiological fitness. The results of this study suggest that foliar application of amino acid biostimulants may improve the summer stress tolerance of cool-season turfgrass species in the U.S. transition zone and other regions with similar climates. 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

In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R² = 0.76), photosystem II efficiency (R² = 0.78), maximum fluorescence (R² = 0.64), and apparent transpiration rate (R² = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R² = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R² = 0.56) and vapor pressure deficit (R² = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R² = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress. Full article

Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha⁻¹), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R² increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R² improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems. Full article

The objective of this study was to evaluate the effects of different water levels on yield, morphological, and quality parameters, as well as the crop water stress index (CWSI), for pepper plants under a high tunnel greenhouse in a semi-arid region. For this purpose, the irrigation schedule used in this study includes 120%, 100%, 80%, and 60% (I120, I100, I80, and I60) of evaporation monitored gravimetrically. In this study, increasing irrigation levels (I100 and I120) resulted in increased stem diameter, plant height, fruit number, leaf number, and leaf area values. However, these values decreased as the water level dropped (I60 and I80). At the same time, increased irrigation resulted in improvements in fruit width, length, and weight, as well as a decrease in TSS values. While total yield and marketable yield values increased at the I120 water level, TWUE and MWUE were the highest at the I100 water level. I80 and I120 water levels were statistically in the same group. It was found that the application of I100 water level in the high tunnel greenhouse is the appropriate irrigation level in terms of morphology and quality parameters. However, in places with water scarcity, a moderate water deficit (I80) can be adopted instead of full (I100) or excessive irrigation (I120) in pepper cultivation in terms of water conservation. The experimental results reveal significant correlations between the parameters of green pepper yield and the CWSI. Therefore, a mean CWSI of 0.16 is recommended for irrigation level I100 for higher-quality yields. A mean CWSI of 0.22 is recommended for irrigation level I80 in areas where water is scarce. While increasing the CWSI values decreased the values of crop water consumption, leaf area index, total yield, marketable yield, total water use efficiency, and marketable water use efficiency, decreasing the CWSI increased these values. This study concluded that the CWSI can be effectively utilised in irrigation time planning under semi-arid climate conditions. With the advancement of technology, determining the CWSI using remote sensing-based methods and integrating it into greenhouse automation systems will become increasingly important in determining irrigation times. Full article

With the increase in prices of correctives and fertilizers, the investigation of the interactions between plants and plant growth-promoting bacteria shows an economically viable and sustainable alternative, and the use of Azospirillum brasilense has shown an increase in efficiency of nitrogen use and increased pasture yield. This study, conducted in the Brazilian Amazon, aimed to evaluate the effect of different inoculation techniques of Azospirillum brasilense associated with the dose of nitrogen topdressing on the productive performance of Brachiaria brizantha cv. Paiaguás is a grass species commonly cultivated in this region. The experiment was conducted in the Experimental Forage Sector of the Federal Rural University of the Amazon, Parauapebas city, Brazil. The experimental design was a randomized block design in a 3 × 3 factorial arrangement, with three inoculation methods (control, seed, and foliar) and three nitrogen fertilization doses (0, 75, and 150 kg ha⁻¹ of N), with four replicates. An effect was observed in interaction between inoculation and nitrogen fertilization (p ≤ 0.05) for the variables total forage green mass, total forage dry mass, dry mass of leaf blade, dry stem mass, and number of tillers m⁻². The dose of 150 kg ha⁻¹ of N promoted a positive effect of N on the total forage dry mass and LAI (leaf area index). Inoculation with Azospirillum brasilense, especially foliar application, efficiently increased Brachiaria brizantha cv. Paiaguás yield, potentially reducing the use of nitrogen fertilizers, promotes greater sustainability in pasture management. 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

Sorghum (Sorghum bicolor, (L.) Moench.), is one of the most important cereals in semi-arid and subtropical regions of Africa. However, in these regions, sorghum cultivation is often faced with several constraints. In Mali, terminal or post-flowering drought, caused by the early cessation of rains towards the end of the rainy season, is one of the most common constraints. Sorghum is generally adapted to harsh conditions. However, drought combined to heat reduce its yield and production in tropical and subtropical regions. To identify parents of sorghum hybrids tolerant to post-flowering drought for commercial hybrids development and deployment, a total of 200 genotypes, including male and female parents of the hybrids, were evaluated in 2022 by lysimeters under two water regimes, well-irrigated and water-stressed, at ICRISAT in Niger. Agronomic traits such as phenological stages, physiological traits including transpiration efficiency, and morphological traits such as green leaf number were recorded. Genotype × environment (G × E) interaction was significant for harvest index (HI), green leaf number (GLN), and transpiration efficiency (TE), indicating different responses of genotypes under varying water conditions. Transpiration efficiency (TE) was significantly and positively correlated with total biomass (BT), harvest index (HI), and grain weight (GW) under both stress conditions. Genotypes ICSV216094, ICSB293, ICSV1049, ICSV1460016, and ICSV216074 performed better under optimal and stress conditions. The Principal Component Analysis (PCA) results led to the identification of three groups of genotypes. The Groups 1 and 3 are characterized by their yield stability and better performance under stress and optimal conditions. These two groups could be used by breeding programs to develop high yield and drought tolerant hybrids. Full article

The management and identification of forest species in a city are essential tasks for current administrations, particularly in planning urban green spaces. However, the cost and time required are typically high. This study evaluates the potential of RGB point clouds captured by unnamed aerial vehicles (UAVs) for automating tree species classification. A dataset of 809 trees (crowns) for eight species was analyzed using a random forest classifier and deep learning with PointNet and PointNet++. In the first case, eleven variables such as the normalized red–blue difference index (NRBDI), intensity, brightness (BI), Green Leaf Index (GLI), points density (normalized), and height (maximum and percentiles 10, 50, and 90), produced the highest reliability values, with an overall accuracy of 0.70 and a Kappa index of 0.65. In the second case, the PointNet model had an overall accuracy of 0.62, and 0.64 with PointNet++; using the features Z, red, green, blue, NRBDI, intensity, and BI. Likewise, there was a high accuracy in the identification of the species Populus alba L., and Melaleuca armillaris (Sol. ex Gaertn.) Sm. This work contributes to a cost-effective workflow for urban tree monitoring using UAV data, comparing classical machine learning with deep learning approaches and analyzing the trade-offs. Full article

Under rainfed Mediterranean-style conditions, crop growth and yield are largely determined by the availability of water. We investigated the role of residue orientation (standing or horizontal) and quantity on temperature, soil water, and wheat growth in two experiments with annual (winter) cropping. In the first trial at Shenton Park, tall (0.3 m) standing residues combined with thick (4 t ha⁻¹) horizontal residues increased the soil water at sowing by more than 100 mm compared with the bare soil control, increasing the wheat yield by about 2 t ha⁻¹. The average soil water storage was linearly related to the total residue quantity (r² = 0.86). Both standing and horizontal residues reduced the daily soil temperature fluctuations, but increased the air temperature fluctuations. Tall-cut residues had higher maximum and lower minimum air temperatures 0.05 m above the ground than short-cut residues with more horizontal material. Under field conditions, more soil water was stored in the growing season with the residues cut relatively tall with less on the ground compared with an equivalent residue amount consisting of shorter residues with more on the ground, although the differences were not great. Tall stubble was also associated with greater green leaf area and PAR interception. At the Cunderdin trial, the residue was greater between the harvester wheel tracks than at the outer edge of the cutting front. Under the very dry seasonal conditions experienced during the trial, greater residue resulted in increased soil water storage, particularly in the top 0.5 m of soil (up to 29 mm), greater green leaf area index, and higher crop yields (up to 300 kg ha⁻¹) behind the harvester, associated with greater spike m⁻², greater spikelets spike⁻¹, and lower root:shoot ratio. These results demonstrate the importance of considering residue orientation to maximise crop water use efficiency and yield. Full article

The content and health impact of harmful heavy metals in agricultural products from strong geological background concentration areas have received increasing attention. To investigate the impact of soil heavy metal contamination on the tea plantation gardens of Guizhou Province, a major tea-producing area with strong geological background concentrations in China, a total of 37 paired soil–tender tea leaf samples (containing one bud and two leaves) were collected and analyzed for eight harmful heavy metals. The results showed that the average contents of Hg, As, Pb, Cd, Cr, Ni, Sb, and Tl in the surface soil (0–20 cm) were 0.26, 23.9, 37.9, 0.29, 75.9, 37, 2.78, and 0.84 mg/kg, respectively. The majority of the soil Hg, As, Pb, Sb, and Tl levels exceeded their background values for cultivated land soil in Guizhou Province to some extent. The geo-accumulation index revealed that Sb and As are the main pollutants of tea garden soil. The average contents of Hg, As, Pb, Cd, Cr, Ni, Sb, and Tl in the tea leaves were 4, 49, 310, 55, 717, 12,100, 30, and 20 μg/kg (on a dry weight basis), respectively, all of which were significantly lower than their national recommended limits for tea. The bioconcentration factors of these eight heavy metals in tea leaves were relatively low when compared with those in soil, ranging between 0.003 (for As) and 0.603 (for Ni). The health risk assessment indicated that the total hazard quotient (THQ) due to drinking tea was in the order of Tl > Ni > As > Pb > Cd >Sb > Hg > Cr, with both the THQ for each heavy metal and the health risk index (HI) being less than 0.29, indicating that the risk of exposure to these heavy metals through drinking Guizhou green tea is low. Although some harmful heavy metals are present in the tea garden soil of Guizhou, their bioavailability for young tea leaves is extremely low. This may be related to the physical and chemical properties of the soil, such as the high proportion of organic matter (up to 9%) which strongly binds with these elements. Full article

Urban trees reduce particulate matter (PM) concentrations through dry deposition, interception, and modifying wind patterns, improving air quality and saving public health expenses in urban planning. The main objective of this article is to present an analysis of the influence of urban trees on PM₁₀ and PM_2.5 concentrations in a high-altitude Latin American megacity (Bogotá, Colombia) using UFORE-D modeling. Six PM monitoring stations distributed throughout the megacity were used. Hourly climatic and PM data were collected for seven years, along with dendrometric and cartographic analyses within 200 m of the monitoring stations. Land cover was quantified using satellite imagery (Landsat 8) in order to perform a spatial analysis. The results showed that the UFORE-D model effectively quantified urban forest canopy area (CA) impact on PM₁₀ and PM_2.5 removal, showing strong correlations (R² = 0.987 and 0.918). PM removal increased with both CA and ambient pollutant concentrations, with CA exhibiting greater influence. Sensitivity analysis highlighted enhanced air quality with increased leaf area index (LAI: 2–4 m²/m²), particularly at higher wind speeds. PM₁₀ removal (1.05 ± 0.01%) per unit CA exceeded PM_2.5 (0.71 ± 0.09%), potentially due to resuspension modeling. Model validation confirmed reliability across urban settings, emphasizing its utility in urban planning. Scenario analysis (E1–E4, CA: 8.30–95.4%) demonstrated a consistent positive correlation between CA and PM removal, with diminishing returns at extreme CA levels. Urban spatial constraints suggested integrated green infrastructure solutions. Although increased CA improved PM removal rates, the absolute reduction of pollutants remained limited, suggesting comprehensive emission monitoring. Full article